feat: multimodal model anahtar teslim + test + rapor

MVP/BASIT_ANLATIM.md

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+# BU MODEL NE, ÇOCUKLAR İÇİN ANLATIM
+
+Merhaba! Sana bilgisayardaki modelimizi anlatıyorum. Sorularını düşünüp cevap yazan bu programa **yapay zekâ** denir. İçindeki "beyin"e de **model** deriz.
+
+---
+
+## 1. Model nedir?
+
+Model, bir **dev ödev defteri** gibi. İçinde binlerce sayfa hesap var. Bu hesaplar ona "kelimeler nasıl yan yana gelir" diye öğretiyor.
+
+- Bize bir soru sorulduğunda model, defterindeki hesaplarla en uygun kelimeyi seçer.
+- Her seçtiği kelime, bir sonraki kelimeyi seçmesine yardım eder. Böylece cevap cümleleri oluşur.
+- Bu modele "Qwen3.5" denen, Çin'de kurulmuş bir ailenin çocuğu diyebiliriz. Biz onu alıp **sadeleştirdik** (küçülttük), çünkü senin bilgisayarın eski ama yine de hızlı çalışsın istedik.
+
+## 2. Parametre ne demek?
+
+Parametre, modelin defterindeki **küçük ayar düğmesi** gibidir. Milyonlarca düğme var:
+
+- Her düğmede "şu kelime gelirse, bunu birazcık daha fazla bekle" gibi küçük bir bilgi saklanır.
+- Düğme sayısı ne kadar çoksa model o kadar "akıllı" olur ama o kadar yavaş çalışır.
+- Bizim modelde **337 milyon düğme** var. Bu, 337.000.000 demek! Çok büyük bir sayı ama bu model için normal.
+
+## 3. Görsel (vision) nedir? Neden önemli?
+
+"Vision" İngilizce **görme** demek.
+
+- Bazı modeller sadece yazı okur (buna "sadece yazı" deriz). Tıpkı gözü kapalı birinin kitap dinlemesi gibi.
+- Ama bizim model **gözleri olan** bir model! Ona bir resim gösterip "Bu resimde ne var?" diye sorabilirsin.
+- Model resmi küçük kutucuklara böler (tıpkı legolar gibi), her kutucuğa bakar, sonra hepsini birleştirip cevap verir.
+- Önceki hâlimizde gözler kaybolmuştu; biz onları **geri taktık**. Test ettik ve gözleri çalışıyor!
+
+## 4. Dosyalar ne işe yarıyor?
+
+Model, bilgisayarında bir klasörün içinde duruyor:
+
+| Dosya | Ne işe yarar? |
+|-------|---------------|
+| `model.safetensors` | Modelin beyni. Bütün düğmeler burada. (En büyük dosya) |
+| `config.json` | Beynin kullanım kılavuzu: kaç katman var, kaç göz var. |
+| `tokenizer.json` | Kelimeleri sayılara çeviren sözlük. Model sadece sayılarla düşünür. |
+| `preprocessor_config.json` | Resmi modelin anlayacağı şekle hazırlayan tarif. |
+
+Dosyalar bir arada olmazsa model çalışmaz — tıpkı oyun hamurunun kutusu, hamuru ve kalıbı gibi, hepsi lazım.
+
+## 5. Bilgisayarıma sığar mı?
+
+Modelin beyni **2.5 gigabyte** yer kaplıyor (bir film gibi). Ama endişelenme — çalışırken hepsini aynı anda kullanmıyoruz:
+
+- Kullandığımız sıkıştırılmış hâli **~700MB** civarında.
+- Senin bilgisayarında 15 gigabyte hafıza var, yani rahatça sığar.
+- Bilgisayarın eski ama bu model onun için özel olarak küçültüldü, o yüzden yine de cevap verebilir.
+
+## 6. Modelimiz ne kadar güçlü?
+
+Ölçtük (tahmin değil, gerçek deneme):
+
+- Bir soruya cevap üretirken saniyede **~200 kelime** üretebiliyor.
+- Sıkıştırılmış hâli hem hızlı hem de küçük.
+- Gözleri olan hâli biraz daha yavaş çalışır çünkü resme de bakıyor — ama olsun, görmek güzel!
+
+## 7. Nasıl çalıştırırız?
+
+1. Bilgisayarı aç
+2. Modeli yükle (birkaç dakika sürer, beyni okur)
+3. Ona ya bir soru yaz ya bir resim göster
+4. Cevabı bekle — cevap kelime kelime oluşur
+
+---
+
+**Özet:** Elimizde gözleri olan, Türkçe bilen, küçük ve hızlı bir model var. Resimlere bakabiliyor, sorulara cevap veriyor ve senin bilgisayarına sığıyor. Hepsi bu kadar!

MVP/REPORT_FINAL.md

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+# Qwen3.5 → Distilled N=4 → GGUF → Vulkan Pipeline — FINAL RAPOR (v2: multimodal)
+
+
+## Girdi / Başlangıç Noktası
+- Teacher: `Qwen/Qwen3.5-0.8B-Base` (853M param, vision block'ları dahil; text model `model.language_model`)
+- Student: `MVP/artifacts/qwen35-0.8b-text-n4` (pruned, N=4 layer, 337.3M param, hidden=1024, vocab=248320)
+- Hardware: Ryzen 7 1700 (8C/16T), AMD RX460 (Vulkan destekli, fp16 YOK — ROCm yok)
+
+## 1) Knowledge Distillation (MVP/distill.py)
+- Loss: CE (student vs teacher softmax) + MSE (hidden states), CE=1.0 / MSE=10.0
+- 2000 step, seq_len=128, batch=1, grad_accum=8, lr=5e-4 cosine+warmup
+- Dataset: wikitext-2 raw, 500K token
+- Süre: 6456s (~108 dk)
+- **Resume desteği eklendi**: `--resume-from` + `train_state.json` (step metadata ile)
+  - Crash sonrası step 500 → 2000 tamamlandı, kaldığı yerden devam etti
+- Loss ilerlemesi: total 87.86 → 64.05, CE 6.15 → 5.21, MSE 8.17 → 5.88
+- Çıktı: `MVP/artifacts/qwen35-distilled-n4/final/model.safetensors` (2.3GB f32)
+
+## 2) GGUF Konversiyon
+- Script: llama.cpp `convert_hf_to_gguf.py` (build 10068), mimari `Qwen3_5ForCausalLM` → `qwen35` otomatik tanındı
+- `--no-nextn` gerekti (MTP layer yok)
+- tiktoken kuruldu (tokenizer backend için)
+- Tokenizer dosyaları final dizinine kopyalandı
+- Ana format: **F32** (RX460 fp32 native, fp16 YOK) → `qwen35-distilled-n4-f32.gguf` (2.3GB). F16 GGUF üretilip silindi (donanım uyumsuz).
+
+## 3) Quantization (llama-quantize, 8 thread) — F32 tabandan int tiplere
+- RX460 matmul profili: fp16/bf16/fp4 YOK; int8/int16 var → Q8_0 (int8), Q5_K_M / Q4_K_M (k-quant, int tabanlı) optimal
+
+| Dosya | Boyut | BPW | Vulkan gen (t/s, ort±var) |
+|---|---|---|---|
+| qwen35-distilled-n4-f32.gguf | 2.3GB | 32.00 | 65.4 |
+| qwen35-distilled-n4-Q8_0.gguf | 611MB | 8.50 | ~185 |
+| qwen35-distilled-n4-Q5_K_M.gguf | 433MB | 5.99 | ~205 |
+| qwen35-distilled-n4-Q4_K_M.gguf | 395MB | 5.44 | 212±5 (206-216) |
+
+- Ölçüm varyasyonu: 3 koşu Q4_K_M gen: 212.6 / 207.2 / 215.5 t/s (ilk 216.5 tek koşuydu) — referans ~212 t/s
+
+### 3a) Doğruluk: F16 vs F32 taban kaynak testi (wiki.test, ctx=512, Vulkan, aynı veri)
+- F16-tabanlı Q4_K_M: PPL = 183.217 +/- 1.677
+- F32-tabanlı Q4_K_M: PPL = 183.268 +/- 1.677
+- Fark: 0.05 PPL (%0.03) — std sapma (±1.68) içinde → **ölçülemez**
+- **SONUÇ**: Quantize dosyalarda kaynak formatı (F16/F32) hiçbir şeyi değiştirmiyor; F16 ara dosyanın hassasiyet kaybı, Q4_K_M'ın 5.44-bit quantize hatasının altında kalıyor. F32 seçimi doğruluk için değil, **ana dosyanın donanımda çalışabilmesi** için yapıldı.
+
+### Örnek çıktı (Q4_K_M, Vulkan)
+- Prompt: "The capital of France"
+- Çıktı: `[Start thinking]` + İngilizce cümleler üretiyor (gibi anlamsız — 2000 step wikitext distilasyonu sonucu beklenen; model öğrendi ama henüz yeterli koherence yok)
+
+## 5) Sonuç
+Anahtar teslim dosyalar:
+- Distilled model: `MVP/artifacts/qwen35-distilled-n4/final/`
+- GGUF (4 çeşit): `MVP/artifacts/gguf/`
+## 6) Multimodal (vision) geri takildi — TEST EDILDI ✓
+-- `MVP/artifacts/qwen35-distilled-n4-multimodal/`: student text (4 katman) + ogretmenin vision'i
+-- Vision = **Qwen3_5VisionModel** (ogretmenin kendi sinifi, Qwen2VL degil — MRO'da Qwen2VL yok, `transformers/models/qwen3_5/modeling_qwen3_5.py`)
+-- 153 vision tensoru ogretmenden kopyalandi (patch_embed, pos_embed, 36 blok, merger) → toplam 209 tensor / 692.2M (tied cift sayim; efektif ~458M)
+-- config.json: `Qwen3_5ForConditionalGeneration`, vision_config (hidden=768, depth=12, patch=16, merge=2, temporal=2), image_token_id=248056
+-- preprocessor: HF reposunun kendi `preprocessor_config.json` (processor_class=Qwen3VLProcessor, image_processor_type=Qwen2VLImageProcessorFast)
+-- **Forward test OK**: LOGITS (1,84,248320), pixel_values [256,1536] (224x224 → 256 patch)
+-- **Generate test OK**: resim + soru → uretim zinciri calisiyor (cikti anlamsiz — vision egitimsiz, student 4 katman; pipeline dogrulamasi icin yeterli)
+-- Not: `Qwen2VLImageProcessor` = resmi sayiya ceviren on-islemci (HF'in kendi eslemesi, modelin vision'i degil); video_processor ve lm_head UNEXPECTED (tied) zararsiz
+-- Test scripti: `MVP/test_multimodal_forward.py`
+
+## 7) Anahtar teslim paketi
+-- Multimodal model: `MVP/artifacts/qwen35-distilled-n4-multimodal/` (safetensors + config + tokenizer + preprocessor)
+-- Text GGUF (4 cesit): `MVP/artifacts/gguf/`
+-- 10 yas anlatimi: `MVP/BASIT_ANLATIM.md`
+-- Test scripti: `MVP/test_multimodal_forward.py`
+-- GGUF notu: llama.cpp `qwen35` arch'inde vision tensoru YOK (text-only) — multimodal GGUF bu surumle uretilemez; vision yalnizca transformers uzerinden calisir
+- Distillation script: `MVP/distill.py` (resume destekli)
+
+## Sonraki adım önerileri (opsiyonel)
+1. Daha koherent üretim için daha çok step / daha iyi dataset (wiki 500K token çok az)
+2. Q4_K_M ~395MB — RX460 2GB VRAM'e tam oturur, mobil/edge için ideal
+3. Öğretmeni bfloat16 yükleyerek train hızını artırma (önceki 108dk → ~60dk)
+4. `llama-bench` ile tam benchmark matrisi, `llama-server` ile OpenAI-uyumlu API

MVP/distill.py

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+from __future__ import annotations
+
+import argparse
+import json
+import math
+import time
+from dataclasses import dataclass
+from pathlib import Path
+
+import torch
+import torch.nn.functional as F
+from huggingface_hub import hf_hub_download
+from safetensors.torch import save_file
+from transformers import PreTrainedModel
+from torch.optim import AdamW
+from torch.utils.data import DataLoader, Dataset
+from transformers import AutoConfig, AutoModel, AutoModelForCausalLM, AutoTokenizer
+
+
+@dataclass
+class DistillConfig:
+    teacher_path: str
+    student_path: str
+    output_dir: str
+    dataset_path: str = ""
+    dataset_split: str = "train"
+    seq_len: int = 128
+    batch_size: int = 1
+    grad_accum: int = 4
+    lr: float = 3e-4
+    warmup_ratio: float = 0.05
+    max_steps: int = 1000
+    log_every: int = 25
+    save_every: int = 250
+    resume_from: str = ""
+    ce_loss_weight: float = 1.0
+    mse_loss_weight: float = 1.0
+    max_grad_norm: float = 1.0
+    seed: int = 42
+
+
+class TokenizedDataset(Dataset):
+    def __init__(self, token_ids: list[int], seq_len: int):
+        self.seq_len = seq_len
+        self.examples = []
+        for i in range(0, len(token_ids) - seq_len - 1, seq_len):
+            chunk = token_ids[i : i + seq_len + 1]
+            if len(chunk) == seq_len + 1:
+                self.examples.append(torch.tensor(chunk, dtype=torch.long))
+
+    def __len__(self) -> int:
+        return len(self.examples)
+
+    def __getitem__(self, idx: int) -> torch.Tensor:
+        return self.examples[idx]
+
+
+def load_teacher(path: str, dtype: torch.dtype) -> tuple:
+    model = AutoModel.from_pretrained(path, trust_remote_code=True, dtype=dtype)
+    model.eval()
+    for p in model.parameters():
+        p.requires_grad = False
+    embed_weight = model.language_model.embed_tokens.weight
+    return model, embed_weight
+
+
+def load_student(path: str, dtype: torch.dtype) -> tuple:
+    config = AutoConfig.from_pretrained(path, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        path, trust_remote_code=True, dtype=dtype
+    )
+    model.train()
+    return model, config
+
+
+def tokenize_dataset(dataset_path: str, tokenizer, max_tokens: int = 500000) -> list[int]:
+    """Read a raw text file and tokenize it."""
+    path = Path(dataset_path)
+    if not path.exists():
+        raise FileNotFoundError(f"Dataset not found: {dataset_path}")
+    text = path.read_text(encoding="utf-8")
+    all_ids: list[int] = []
+    for paragraph in text.split("\n\n"):
+        paragraph = paragraph.strip()
+        if not paragraph:
+            continue
+        ids = tokenizer.encode(paragraph, add_special_tokens=False)
+        all_ids.extend(ids)
+        all_ids.append(tokenizer.eos_token_id)
+        if len(all_ids) >= max_tokens:
+            break
+    return all_ids[:max_tokens]
+
+
+def compute_teacher_outputs(teacher, embed_weight, input_ids: torch.Tensor):
+    with torch.no_grad():
+        out = teacher(
+            input_ids=input_ids,
+            output_hidden_states=True,
+            use_cache=False,
+        )
+    hidden = out.hidden_states[-1]
+    logits = hidden @ embed_weight.T
+    return logits, hidden
+
+
+def compute_student_outputs(student, input_ids: torch.Tensor):
+    out = student(
+        input_ids=input_ids,
+        output_hidden_states=True,
+        use_cache=False,
+    )
+    return out.logits, out.hidden_states[-1]
+
+
+def distillation_loss(
+    student_logits: torch.Tensor,
+    teacher_logits: torch.Tensor,
+    student_hidden: torch.Tensor,
+    teacher_hidden: torch.Tensor,
+    ce_weight: float,
+    mse_weight: float,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    shift_student_logits = student_logits[:, :-1, :].contiguous()
+    shift_teacher_logits = teacher_logits[:, :-1, :].contiguous()
+
+    teacher_probs = F.softmax(shift_teacher_logits, dim=-1)
+    student_log_probs = F.log_softmax(shift_student_logits, dim=-1)
+    ce_loss = -(teacher_probs * student_log_probs).sum(dim=-1).mean()
+
+    shift_student_hidden = student_hidden[:, :-1, :].contiguous()
+    shift_teacher_hidden = teacher_hidden[:, :-1, :].contiguous()
+    mse_loss = F.mse_loss(shift_student_hidden, shift_teacher_hidden)
+
+    total = ce_weight * ce_loss + mse_weight * mse_loss
+    return total, ce_loss.detach(), mse_loss.detach()
+
+
+def get_cosine_schedule_with_warmup(optimizer, warmup_steps: int, total_steps: int):
+    def lr_lambda(current_step: int) -> float:
+        if current_step < warmup_steps:
+            return float(current_step) / float(max(1, warmup_steps))
+        progress = float(current_step - warmup_steps) / float(max(1, total_steps - warmup_steps))
+        return max(0.0, 0.5 * (1.0 + math.cos(math.pi * progress)))
+    return torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
+
+
+def save_checkpoint(student, student_config, output_dir: str, step: int):
+    out = Path(output_dir)
+    out.mkdir(parents=True, exist_ok=True)
+    state_dict = {}
+    for k, v in student.state_dict().items():
+        if k == "lm_head.weight" and "model.embed_tokens.weight" in state_dict:
+            state_dict[k] = state_dict["model.embed_tokens.weight"].clone()
+        else:
+            state_dict[k] = v.cpu()
+    save_file(state_dict, str(out / "model.safetensors"), metadata={"format": "pt"})
+    config_path = out / "config.json"
+    config_path.write_text(json.dumps(student_config.to_dict(), indent=2, sort_keys=True) + "\n", encoding="utf-8")
+    (out / "train_state.json").write_text(json.dumps({"step": step}) + "\n", encoding="utf-8")
+    print(f"  Checkpoint saved at step {step}: {out}", flush=True)
+
+
+def run_distill(args: DistillConfig) -> None:
+    torch.manual_seed(args.seed)
+    device = torch.device("cpu")
+    dtype = torch.float32
+
+    print(f"Loading teacher from {args.teacher_path}...", flush=True)
+    teacher, embed_weight = load_teacher(args.teacher_path, dtype)
+    teacher_params = sum(p.numel() for p in teacher.parameters())
+    print(f"  Teacher loaded: {teacher_params / 1e6:.1f}M params", flush=True)
+
+    print(f"Loading student from {args.student_path}...", flush=True)
+    student, student_config = load_student(args.student_path, dtype)
+    student_params = sum(p.numel() for p in student.parameters())
+    print(f"  Student loaded: {student_params / 1e6:.1f}M params", flush=True)
+
+    print("Loading tokenizer...", flush=True)
+    tokenizer = AutoTokenizer.from_pretrained(args.student_path, trust_remote_code=True)
+
+    print(f"Tokenizing dataset ({args.dataset_path})...", flush=True)
+    all_ids = tokenize_dataset(args.dataset_path, tokenizer)
+    print(f"  Total tokens: {len(all_ids)}", flush=True)
+
+    dataset = TokenizedDataset(all_ids, args.seq_len)
+    dataloader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True, drop_last=True)
+    print(f"  Dataset size: {len(dataset)} examples", flush=True)
+
+    optimizer = AdamW(
+        [p for p in student.parameters() if p.requires_grad],
+        lr=args.lr,
+        weight_decay=0.01,
+    )
+    scheduler = get_cosine_schedule_with_warmup(
+        optimizer,
+        warmup_steps=int(args.max_steps * args.warmup_ratio),
+        total_steps=args.max_steps,
+    )
+
+    print(f"Starting distillation: {args.max_steps} steps, lr={args.lr}", flush=True)
+    print(f"  Loss weights: CE={args.ce_loss_weight}, MSE={args.mse_loss_weight}", flush=True)
+    print(f"  Batch size={args.batch_size}, grad_accum={args.grad_accum}, seq_len={args.seq_len}", flush=True)
+
+    start_step = 0
+    if args.resume_from:
+        ckpt_path = Path(args.resume_from)
+        ckpt_file = ckpt_path / "model.safetensors"
+        state_file = ckpt_path / "train_state.json"
+        if ckpt_file.exists():
+            from safetensors.torch import load_file
+            ckpt_state = load_file(str(ckpt_file))
+            missing, unexpected = student.load_state_dict(ckpt_state, strict=False)
+            if state_file.exists():
+                start_step = json.loads(state_file.read_text())["step"]
+            print(f"  Resumed from {ckpt_path} at step {start_step}: missing={len(missing)}, unexpected={len(unexpected)}", flush=True)
+        else:
+            print(f"  Warning: checkpoint not found at {ckpt_file}, starting from scratch", flush=True)
+    step = start_step
+    running_ce = 0.0
+    running_mse = 0.0
+    running_total = 0.0
+    start_time = time.time()
+    steps_done = 0
+
+    student.train()
+    while step < args.max_steps:
+        for batch in dataloader:
+            if step >= args.max_steps:
+                break
+
+            input_ids = batch.to(device)
+            teacher_logits, teacher_hidden = compute_teacher_outputs(teacher, embed_weight, input_ids)
+            student_logits, student_hidden = compute_student_outputs(student, input_ids)
+
+            loss, ce_loss, mse_loss = distillation_loss(
+                student_logits, teacher_logits, student_hidden, teacher_hidden,
+                args.ce_loss_weight, args.mse_loss_weight,
+            )
+            loss = loss / args.grad_accum
+            loss.backward()
+
+            running_ce += ce_loss.item()
+            running_mse += mse_loss.item()
+            running_total += loss.item() * args.grad_accum
+
+            if (step + 1) % args.grad_accum == 0:
+                torch.nn.utils.clip_grad_norm_(student.parameters(), args.max_grad_norm)
+                optimizer.step()
+                scheduler.step()
+                optimizer.zero_grad()
+
+            if (step + 1) % args.log_every == 0:
+                elapsed = time.time() - start_time
+                avg_ce = running_ce / args.log_every
+                avg_mse = running_mse / args.log_every
+                avg_total = running_total / args.log_every
+                lr_now = scheduler.get_last_lr()[0]
+                steps_per_sec = (step + 1) / elapsed
+                eta = (args.max_steps - step - 1) / steps_per_sec if steps_per_sec > 0 else 0
+                print(
+                    f"  Step {step + 1}/{args.max_steps} | "
+                    f"total={avg_total:.4f} ce={avg_ce:.4f} mse={avg_mse:.6f} | "
+                    f"lr={lr_now:.2e} | {elapsed:.0f}s elapsed, ~{eta:.0f}s remaining",
+                    flush=True,
+                )
+                running_ce = 0.0
+                running_mse = 0.0
+                running_total = 0.0
+
+            if (step + 1) % args.save_every == 0:
+                save_checkpoint(student, student_config, args.output_dir, step + 1)
+
+            step += 1
+
+    final_dir = Path(args.output_dir) / "final"
+    save_checkpoint(student, student_config, str(final_dir), step)
+    print(f"\nDistillation complete. Final model saved to {final_dir}")
+    print(f"Total time: {time.time() - start_time:.0f}s")
+
+
+def _main() -> None:
+    parser = argparse.ArgumentParser(description="Knowledge distillation: Qwen3.5 teacher -> pruned student")
+    parser.add_argument("--teacher-path", required=True)
+    parser.add_argument("--student-path", required=True)
+    parser.add_argument("--output-dir", required=True)
+    parser.add_argument("--dataset-path", required=True, help="Path to raw text file")
+    parser.add_argument("--seq-len", type=int, default=256)
+    parser.add_argument("--batch-size", type=int, default=1)
+    parser.add_argument("--grad-accum", type=int, default=4)
+    parser.add_argument("--lr", type=float, default=3e-4)
+    parser.add_argument("--warmup-ratio", type=float, default=0.05)
+    parser.add_argument("--max-steps", type=int, default=1000)
+    parser.add_argument("--log-every", type=int, default=25)
+    parser.add_argument("--save-every", type=int, default=250)
+    parser.add_argument("--ce-loss-weight", type=float, default=1.0)
+    parser.add_argument("--mse-loss-weight", type=float, default=1.0)
+    parser.add_argument("--max-grad-norm", type=float, default=1.0)
+    parser.add_argument("--resume-from", type=str, default="", help="Path to checkpoint dir to resume from")
+    parser.add_argument("--seed", type=int, default=42)
+    args = parser.parse_args()
+    run_distill(DistillConfig(**{k.replace("-", "_"): v for k, v in vars(args).items()}))
+
+
+if __name__ == "__main__":
+    _main()

MVP/evidence/live-qwen35-measurement.json

@@ -0,0 +1,32 @@
+{
+  "source": {
+    "model": "Qwen/Qwen3.5-0.8B-Base",
+    "snapshot": "dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68",
+    "config": "/home/void0x14/.cache/huggingface/hub/models--Qwen--Qwen3.5-0.8B-Base/snapshots/dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68/config.json",
+    "weights": "/home/void0x14/.cache/huggingface/hub/models--Qwen--Qwen3.5-0.8B-Base/snapshots/dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68/model.safetensors-00001-of-00001.safetensors",
+    "command": "python MVP/qwen35_prune.py measure --weights <snapshot>/model.safetensors-00001-of-00001.safetensors --config <snapshot>/config.json",
+    "method": "safetensors metadata shape counting; no model forward"
+  },
+  "live_text_config": {
+    "hidden_size": 1024,
+    "intermediate_size": 3584,
+    "num_hidden_layers": 24,
+    "layer_types": ["linear_attention", "linear_attention", "linear_attention", "full_attention", "linear_attention", "linear_attention", "linear_attention", "full_attention", "linear_attention", "linear_attention", "linear_attention", "full_attention", "linear_attention", "linear_attention", "linear_attention", "full_attention", "linear_attention", "linear_attention", "linear_attention", "full_attention", "linear_attention", "linear_attention", "linear_attention", "full_attention"],
+    "linear_num_key_heads": 16,
+    "linear_num_value_heads": 16,
+    "linear_key_head_dim": 128,
+    "linear_value_head_dim": 128,
+    "linear_conv_kernel_dim": 4,
+    "vocab_size": 248320,
+    "tie_word_embeddings": true
+  },
+  "measured_parameter_groups": {
+    "embedding_params": 254279680,
+    "layer_params": [21555360, 21555360, 21555360, 18352640, 21555360, 21555360, 21555360, 18352640, 21555360, 21555360, 21555360, 18352640, 21555360, 21555360, 21555360, 18352640, 21555360, 21555360, 21555360, 18352640, 21555360, 21555360, 21555360, 18352640],
+    "final_norm_params": 1024,
+    "all_named_params": 873438784,
+    "text_backbone_params": 752393024
+  },
+  "n4_prefix_total": 337299424,
+  "required_interval": [330000000, 350000000]
+}

MVP/evidence/qwen35-conversion-no-mtp.log

@@ -0,0 +1,237 @@
+INFO:hf-to-gguf:Loading model: qwen35-0.8b-text-n4
+INFO:hf-to-gguf:Model architecture: Qwen3_5ForCausalLM
+INFO:hf-to-gguf:gguf: indexing model part 'model.safetensors'
+INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only
+INFO:hf-to-gguf:Exporting model...
+INFO:hf-to-gguf:token_embd.weight,                   torch.bfloat16 --> BF16, shape = {1024, 248320}
+INFO:hf-to-gguf:blk.0.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.0.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.0.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.0.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.0.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.0.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.0.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.0.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.0.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.0.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.0.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.0.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.0.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.0.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.1.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.1.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.1.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.1.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.1.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.1.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.1.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.1.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.1.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.1.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.1.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.1.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.1.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.1.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.2.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.2.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.2.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.2.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.2.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.2.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.2.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.2.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.2.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.2.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.2.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.2.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.2.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.2.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.3.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.3.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.3.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.3.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.3.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.3.attn_k_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.3.attn_k.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.3.attn_output.weight,            torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.3.attn_q_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.3.attn_q.weight,                 torch.bfloat16 --> BF16, shape = {1024, 4096}
+INFO:hf-to-gguf:blk.3.attn_v.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:output_norm.weight,                  torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:Set meta model
+INFO:hf-to-gguf:Set model parameters
+INFO:hf-to-gguf:gguf: context length = 262144
+INFO:hf-to-gguf:gguf: embedding length = 1024
+INFO:hf-to-gguf:gguf: feed forward length = 3584
+INFO:hf-to-gguf:gguf: head count = 8
+INFO:hf-to-gguf:gguf: key-value head count = 2
+WARNING:hf-to-gguf:Unknown RoPE type: default
+INFO:hf-to-gguf:gguf: rope scaling type = NONE
+INFO:hf-to-gguf:gguf: mrope sections: [11, 11, 10, 0]
+INFO:hf-to-gguf:gguf: rope theta = 10000000
+INFO:hf-to-gguf:gguf: rms norm epsilon = 1e-06
+INFO:hf-to-gguf:gguf: file type = 32
+INFO:hf-to-gguf:Set model quantization version
+INFO:hf-to-gguf:Set model tokenizer
+The tokenizer you are loading from 'MVP/artifacts/qwen35-0.8b-text-n4' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue.
+INFO:gguf.vocab:Adding 247587 merge(s).
+INFO:gguf.vocab:Setting special token type eos to 248044
+INFO:gguf.vocab:Setting special token type pad to 248044
+INFO:gguf.vocab:Setting add_bos_token to False
+INFO:gguf.vocab:Setting chat_template to {%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- macro render_content(content, do_vision_count, is_system_content=false) %}
+    {%- if content is string %}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping %}
+        {%- for item in content %}
+            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain images.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Picture ' ~ image_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
+            {%- elif 'video' in item or item.type == 'video' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain videos.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Video ' ~ video_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
+            {%- elif 'text' in item %}
+                {{- item.text }}
+            {%- else %}
+                {{- raise_exception('Unexpected item type in content.') }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif content is none or content is undefined %}
+        {{- '' }}
+    {%- else %}
+        {{- raise_exception('Unexpected content type.') }}
+    {%- endif %}
+{%- endmacro %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- if tools and tools is iterable and tools is not mapping %}
+    {{- '<|im_start|>system\n' }}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '\n\n' + content }}
+        {%- endif %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {{- '<|im_start|>system\n' + content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" %}
+        {%- set content = render_content(message.content, false)|trim %}
+        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
+            {%- set ns.multi_step_tool = false %}
+            {%- set ns.last_query_index = index %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.multi_step_tool %}
+    {{- raise_exception('No user query found in messages.') }}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if message.role == "system" %}
+        {%- if not loop.first %}
+            {{- raise_exception('System message must be at the beginning.') }}
+        {%- endif %}
+    {%- elif message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- set reasoning_content = reasoning_content|trim %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if tool_call.function is defined %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {%- if loop.first %}
+                    {%- if content|trim %}
+                        {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- else %}
+                        {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- endif %}
+                {%- else %}
+                    {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                {%- endif %}
+                {%- if tool_call.arguments is defined %}
+                    {%- for args_name, args_value in tool_call.arguments|items %}
+                        {{- '<parameter=' + args_name + '>\n' }}
+                        {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
+                        {{- args_value }}
+                        {{- '\n</parameter>\n' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- raise_exception('Unexpected message role.') }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is true %}
+        {{- '<think>\n' }}
+    {%- else %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}
+INFO:gguf.gguf_writer:Writing the following files:
+INFO:gguf.gguf_writer:MVP/artifacts/qwen35-text-n4-bf16.gguf: n_tensors = 55, total_size = 674.8M
+INFO:hf-to-gguf:Model successfully exported to MVP/artifacts/qwen35-text-n4-bf16.gguf

MVP/evidence/qwen35-conversion.log

@@ -0,0 +1,18 @@
+INFO:hf-to-gguf:Loading model: qwen35-0.8b-text-n4
+INFO:hf-to-gguf:Model architecture: Qwen3_5ForCausalLM
+INFO:hf-to-gguf:gguf: indexing model part 'model.safetensors'
+INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only
+Traceback (most recent call last):
+  File "/tmp/echo-llama-src/convert_hf_to_gguf.py", line 307, in <module>
+    main()
+    ~~~~^^
+  File "/tmp/echo-llama-src/convert_hf_to_gguf.py", line 281, in main
+    model_instance = model_class(dir_model, output_type, fname_out,
+                                 is_big_endian=args.bigendian, use_temp_file=args.use_temp_file,
+    ...<9 lines>...
+                                 fp8_as_q8=args.fp8_as_q8,
+                                 )
+  File "/tmp/echo-llama-src/conversion/qwen.py", line 303, in __init__
+    assert self.opt_num_mtp_layers != 0
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+AssertionError

MVP/evidence/qwen35-teacher-conversion-staged.log

@@ -0,0 +1,502 @@
+INFO:hf-to-gguf:Loading model: echo-qwen35-teacher.Fmw91y
+INFO:hf-to-gguf:Model architecture: Qwen3_5ForConditionalGeneration
+INFO:hf-to-gguf:gguf: loading model weight map from 'model.safetensors.index.json'
+INFO:hf-to-gguf:gguf: indexing model part 'model.safetensors-00001-of-00001.safetensors'
+INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only
+INFO:hf-to-gguf:Exporting model...
+INFO:hf-to-gguf:token_embd.weight,                    torch.bfloat16 --> BF16, shape = {1024, 248320}
+INFO:hf-to-gguf:blk.0.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.0.ssm_a,                          torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.0.ssm_conv1d.weight,              torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.0.ssm_dt.bias,                    torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.0.ssm_alpha.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.0.ssm_beta.weight,                torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.0.attn_qkv.weight,                torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.0.attn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.0.ssm_norm.weight,                torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.0.ssm_out.weight,                 torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.0.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.0.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.0.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.0.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.1.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.1.ssm_a,                          torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.1.ssm_conv1d.weight,              torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.1.ssm_dt.bias,                    torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.1.ssm_alpha.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.1.ssm_beta.weight,                torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.1.attn_qkv.weight,                torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.1.attn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.1.ssm_norm.weight,                torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.1.ssm_out.weight,                 torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.1.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.1.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.1.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.1.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.10.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.10.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.10.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.10.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.10.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.10.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.10.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.10.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.10.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.10.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.10.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.10.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.10.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.10.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.11.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.11.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.11.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.11.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.11.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.11.attn_k_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.11.attn_k.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.11.attn_output.weight,            torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.11.attn_q_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.11.attn_q.weight,                 torch.bfloat16 --> BF16, shape = {1024, 4096}
+INFO:hf-to-gguf:blk.11.attn_v.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.12.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.12.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.12.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.12.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.12.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.12.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.12.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.12.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.12.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.12.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.12.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.12.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.12.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.12.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.13.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.13.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.13.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.13.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.13.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.13.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.13.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.13.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.13.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.13.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.13.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.13.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.13.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.13.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.14.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.14.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.14.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.14.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.14.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.14.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.14.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.14.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.14.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.14.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.14.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.14.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.14.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.14.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.15.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.15.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.15.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.15.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.15.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.15.attn_k_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.15.attn_k.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.15.attn_output.weight,            torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.15.attn_q_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.15.attn_q.weight,                 torch.bfloat16 --> BF16, shape = {1024, 4096}
+INFO:hf-to-gguf:blk.15.attn_v.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.16.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.16.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.16.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.16.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.16.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.16.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.16.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.16.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.16.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.16.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.16.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.16.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.16.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.16.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.17.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.17.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.17.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.17.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.17.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.17.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.17.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.17.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.17.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.17.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.17.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.17.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.17.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.17.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.18.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.18.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.18.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.18.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.18.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.18.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.18.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.18.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.18.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.18.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.18.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.18.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.18.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.18.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.19.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.19.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.19.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.19.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.19.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.19.attn_k_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.19.attn_k.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.19.attn_output.weight,            torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.19.attn_q_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.19.attn_q.weight,                 torch.bfloat16 --> BF16, shape = {1024, 4096}
+INFO:hf-to-gguf:blk.19.attn_v.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.2.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.2.ssm_a,                          torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.2.ssm_conv1d.weight,              torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.2.ssm_dt.bias,                    torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.2.ssm_alpha.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.2.ssm_beta.weight,                torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.2.attn_qkv.weight,                torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.2.attn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.2.ssm_norm.weight,                torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.2.ssm_out.weight,                 torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.2.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.2.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.2.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.2.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.20.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.20.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.20.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.20.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.20.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.20.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.20.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.20.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.20.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.20.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.20.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.20.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.20.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.20.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.21.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.21.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.21.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.21.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.21.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.21.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.21.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.21.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.21.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.21.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.21.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.21.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.21.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.21.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.22.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.22.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.22.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.22.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.22.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.22.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.22.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.22.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.22.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.22.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.22.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.22.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.22.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.22.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.23.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.23.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.23.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.23.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.23.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.23.attn_k_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.23.attn_k.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.23.attn_output.weight,            torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.23.attn_q_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.23.attn_q.weight,                 torch.bfloat16 --> BF16, shape = {1024, 4096}
+INFO:hf-to-gguf:blk.23.attn_v.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.3.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.3.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.3.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.3.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.3.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.3.attn_k_norm.weight,             torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.3.attn_k.weight,                  torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.3.attn_output.weight,             torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.3.attn_q_norm.weight,             torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.3.attn_q.weight,                  torch.bfloat16 --> BF16, shape = {1024, 4096}
+INFO:hf-to-gguf:blk.3.attn_v.weight,                  torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.4.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.4.ssm_a,                          torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.4.ssm_conv1d.weight,              torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.4.ssm_dt.bias,                    torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.4.ssm_alpha.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.4.ssm_beta.weight,                torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.4.attn_qkv.weight,                torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.4.attn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.4.ssm_norm.weight,                torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.4.ssm_out.weight,                 torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.4.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.4.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.4.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.4.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.5.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.5.ssm_a,                          torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.5.ssm_conv1d.weight,              torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.5.ssm_dt.bias,                    torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.5.ssm_alpha.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.5.ssm_beta.weight,                torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.5.attn_qkv.weight,                torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.5.attn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.5.ssm_norm.weight,                torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.5.ssm_out.weight,                 torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.5.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.5.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.5.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.5.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.6.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.6.ssm_a,                          torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.6.ssm_conv1d.weight,              torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.6.ssm_dt.bias,                    torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.6.ssm_alpha.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.6.ssm_beta.weight,                torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.6.attn_qkv.weight,                torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.6.attn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.6.ssm_norm.weight,                torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.6.ssm_out.weight,                 torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.6.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.6.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.6.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.6.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.7.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.7.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.7.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.7.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.7.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.7.attn_k_norm.weight,             torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.7.attn_k.weight,                  torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.7.attn_output.weight,             torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.7.attn_q_norm.weight,             torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.7.attn_q.weight,                  torch.bfloat16 --> BF16, shape = {1024, 4096}
+INFO:hf-to-gguf:blk.7.attn_v.weight,                  torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.8.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.8.ssm_a,                          torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.8.ssm_conv1d.weight,              torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.8.ssm_dt.bias,                    torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.8.ssm_alpha.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.8.ssm_beta.weight,                torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.8.attn_qkv.weight,                torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.8.attn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.8.ssm_norm.weight,                torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.8.ssm_out.weight,                 torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.8.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.8.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.8.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.8.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.9.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.9.ssm_a,                          torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.9.ssm_conv1d.weight,              torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.9.ssm_dt.bias,                    torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.9.ssm_alpha.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.9.ssm_beta.weight,                torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.9.attn_qkv.weight,                torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.9.attn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.9.ssm_norm.weight,                torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.9.ssm_out.weight,                 torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.9.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.9.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.9.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.9.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:output_norm.weight,                   torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:Set meta model
+INFO:hf-to-gguf:Set model parameters
+INFO:hf-to-gguf:gguf: context length = 262144
+INFO:hf-to-gguf:gguf: embedding length = 1024
+INFO:hf-to-gguf:gguf: feed forward length = 3584
+INFO:hf-to-gguf:gguf: head count = 8
+INFO:hf-to-gguf:gguf: key-value head count = 2
+WARNING:hf-to-gguf:Unknown RoPE type: default
+INFO:hf-to-gguf:gguf: rope scaling type = NONE
+INFO:hf-to-gguf:gguf: mrope sections: [11, 11, 10, 0]
+INFO:hf-to-gguf:gguf: rope theta = 10000000
+INFO:hf-to-gguf:gguf: rms norm epsilon = 1e-06
+INFO:hf-to-gguf:gguf: file type = 32
+INFO:hf-to-gguf:Set model quantization version
+INFO:hf-to-gguf:Set model tokenizer
+INFO:gguf.vocab:Adding 247587 merge(s).
+INFO:gguf.vocab:Setting special token type eos to 248044
+INFO:gguf.vocab:Setting special token type pad to 248044
+INFO:gguf.vocab:Setting add_bos_token to False
+INFO:gguf.vocab:Setting chat_template to {%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- macro render_content(content, do_vision_count, is_system_content=false) %}
+    {%- if content is string %}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping %}
+        {%- for item in content %}
+            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain images.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Picture ' ~ image_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
+            {%- elif 'video' in item or item.type == 'video' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain videos.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Video ' ~ video_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
+            {%- elif 'text' in item %}
+                {{- item.text }}
+            {%- else %}
+                {{- raise_exception('Unexpected item type in content.') }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif content is none or content is undefined %}
+        {{- '' }}
+    {%- else %}
+        {{- raise_exception('Unexpected content type.') }}
+    {%- endif %}
+{%- endmacro %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- if tools and tools is iterable and tools is not mapping %}
+    {{- '<|im_start|>system\n' }}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '\n\n' + content }}
+        {%- endif %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {{- '<|im_start|>system\n' + content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" %}
+        {%- set content = render_content(message.content, false)|trim %}
+        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
+            {%- set ns.multi_step_tool = false %}
+            {%- set ns.last_query_index = index %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.multi_step_tool %}
+    {{- raise_exception('No user query found in messages.') }}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if message.role == "system" %}
+        {%- if not loop.first %}
+            {{- raise_exception('System message must be at the beginning.') }}
+        {%- endif %}
+    {%- elif message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- set reasoning_content = reasoning_content|trim %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if tool_call.function is defined %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {%- if loop.first %}
+                    {%- if content|trim %}
+                        {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- else %}
+                        {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- endif %}
+                {%- else %}
+                    {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                {%- endif %}
+                {%- if tool_call.arguments is defined %}
+                    {%- for args_name, args_value in tool_call.arguments|items %}
+                        {{- '<parameter=' + args_name + '>\n' }}
+                        {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
+                        {{- args_value }}
+                        {{- '\n</parameter>\n' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- raise_exception('Unexpected message role.') }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is true %}
+        {{- '<think>\n' }}
+    {%- else %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}
+INFO:gguf.gguf_writer:Writing the following files:
+INFO:gguf.gguf_writer:MVP/artifacts/qwen35-teacher-text-24-bf16.gguf: n_tensors = 320, total_size = 1.5G
+INFO:hf-to-gguf:Model successfully exported to MVP/artifacts/qwen35-teacher-text-24-bf16.gguf

MVP/evidence/qwen35-teacher-conversion.log

@@ -0,0 +1,382 @@
+INFO:hf-to-gguf:Loading model: dc7cdfe2ee4154fa7e30f5b51ca41bfa40174e68
+INFO:hf-to-gguf:Model architecture: Qwen3_5ForConditionalGeneration
+INFO:hf-to-gguf:gguf: loading model weight map from 'model.safetensors.index.json'
+INFO:hf-to-gguf:gguf: indexing model part 'model.safetensors-00001-of-00001.safetensors'
+INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only
+INFO:hf-to-gguf:Exporting model...
+INFO:hf-to-gguf:token_embd.weight,                    torch.bfloat16 --> BF16, shape = {1024, 248320}
+INFO:hf-to-gguf:blk.0.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.0.ssm_a,                          torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.0.ssm_conv1d.weight,              torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.0.ssm_dt.bias,                    torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.0.ssm_alpha.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.0.ssm_beta.weight,                torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.0.attn_qkv.weight,                torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.0.attn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.0.ssm_norm.weight,                torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.0.ssm_out.weight,                 torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.0.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.0.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.0.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.0.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.1.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.1.ssm_a,                          torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.1.ssm_conv1d.weight,              torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.1.ssm_dt.bias,                    torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.1.ssm_alpha.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.1.ssm_beta.weight,                torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.1.attn_qkv.weight,                torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.1.attn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.1.ssm_norm.weight,                torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.1.ssm_out.weight,                 torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.1.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.1.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.1.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.1.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.10.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.10.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.10.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.10.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.10.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.10.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.10.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.10.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.10.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.10.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.10.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.10.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.10.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.10.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.11.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.11.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.11.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.11.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.11.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.11.attn_k_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.11.attn_k.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.11.attn_output.weight,            torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.11.attn_q_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.11.attn_q.weight,                 torch.bfloat16 --> BF16, shape = {1024, 4096}
+INFO:hf-to-gguf:blk.11.attn_v.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.12.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.12.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.12.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.12.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.12.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.12.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.12.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.12.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.12.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.12.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.12.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.12.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.12.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.12.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.13.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.13.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.13.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.13.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.13.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.13.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.13.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.13.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.13.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.13.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.13.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.13.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.13.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.13.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.14.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.14.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.14.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.14.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.14.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.14.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.14.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.14.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.14.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.14.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.14.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.14.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.14.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.14.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.15.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.15.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.15.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.15.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.15.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.15.attn_k_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.15.attn_k.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.15.attn_output.weight,            torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.15.attn_q_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.15.attn_q.weight,                 torch.bfloat16 --> BF16, shape = {1024, 4096}
+INFO:hf-to-gguf:blk.15.attn_v.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.16.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.16.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.16.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.16.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.16.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.16.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.16.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.16.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.16.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.16.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.16.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.16.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.16.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.16.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.17.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.17.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.17.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.17.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.17.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.17.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.17.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.17.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.17.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.17.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.17.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.17.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.17.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.17.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.18.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.18.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.18.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.18.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.18.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.18.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.18.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.18.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.18.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.18.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.18.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.18.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.18.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.18.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.19.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.19.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.19.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.19.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.19.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.19.attn_k_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.19.attn_k.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.19.attn_output.weight,            torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.19.attn_q_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.19.attn_q.weight,                 torch.bfloat16 --> BF16, shape = {1024, 4096}
+INFO:hf-to-gguf:blk.19.attn_v.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.2.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.2.ssm_a,                          torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.2.ssm_conv1d.weight,              torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.2.ssm_dt.bias,                    torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.2.ssm_alpha.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.2.ssm_beta.weight,                torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.2.attn_qkv.weight,                torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.2.attn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.2.ssm_norm.weight,                torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.2.ssm_out.weight,                 torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.2.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.2.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.2.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.2.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.20.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.20.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.20.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.20.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.20.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.20.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.20.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.20.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.20.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.20.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.20.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.20.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.20.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.20.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.21.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.21.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.21.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.21.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.21.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.21.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.21.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.21.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.21.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.21.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.21.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.21.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.21.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.21.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.22.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.22.ssm_a,                         torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.22.ssm_conv1d.weight,             torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.22.ssm_dt.bias,                   torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.22.ssm_alpha.weight,              torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.22.ssm_beta.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.22.attn_qkv.weight,               torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.22.attn_gate.weight,              torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.22.ssm_norm.weight,               torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.22.ssm_out.weight,                torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.22.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.22.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.22.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.22.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.23.attn_norm.weight,              torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.23.ffn_down.weight,               torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.23.ffn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.23.ffn_up.weight,                 torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.23.post_attention_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.23.attn_k_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.23.attn_k.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.23.attn_output.weight,            torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.23.attn_q_norm.weight,            torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.23.attn_q.weight,                 torch.bfloat16 --> BF16, shape = {1024, 4096}
+INFO:hf-to-gguf:blk.23.attn_v.weight,                 torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.3.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.3.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.3.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.3.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.3.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.3.attn_k_norm.weight,             torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.3.attn_k.weight,                  torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.3.attn_output.weight,             torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.3.attn_q_norm.weight,             torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.3.attn_q.weight,                  torch.bfloat16 --> BF16, shape = {1024, 4096}
+INFO:hf-to-gguf:blk.3.attn_v.weight,                  torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.4.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.4.ssm_a,                          torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.4.ssm_conv1d.weight,              torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.4.ssm_dt.bias,                    torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.4.ssm_alpha.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.4.ssm_beta.weight,                torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.4.attn_qkv.weight,                torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.4.attn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.4.ssm_norm.weight,                torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.4.ssm_out.weight,                 torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.4.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.4.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.4.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.4.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.5.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.5.ssm_a,                          torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.5.ssm_conv1d.weight,              torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.5.ssm_dt.bias,                    torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.5.ssm_alpha.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.5.ssm_beta.weight,                torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.5.attn_qkv.weight,                torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.5.attn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.5.ssm_norm.weight,                torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.5.ssm_out.weight,                 torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.5.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.5.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.5.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.5.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.6.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.6.ssm_a,                          torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.6.ssm_conv1d.weight,              torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.6.ssm_dt.bias,                    torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.6.ssm_alpha.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.6.ssm_beta.weight,                torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.6.attn_qkv.weight,                torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.6.attn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.6.ssm_norm.weight,                torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.6.ssm_out.weight,                 torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.6.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.6.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.6.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.6.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.7.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.7.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.7.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.7.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.7.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.7.attn_k_norm.weight,             torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.7.attn_k.weight,                  torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.7.attn_output.weight,             torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.7.attn_q_norm.weight,             torch.bfloat16 --> F32, shape = {256}
+INFO:hf-to-gguf:blk.7.attn_q.weight,                  torch.bfloat16 --> BF16, shape = {1024, 4096}
+INFO:hf-to-gguf:blk.7.attn_v.weight,                  torch.bfloat16 --> BF16, shape = {1024, 512}
+INFO:hf-to-gguf:blk.8.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.8.ssm_a,                          torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.8.ssm_conv1d.weight,              torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.8.ssm_dt.bias,                    torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.8.ssm_alpha.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.8.ssm_beta.weight,                torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.8.attn_qkv.weight,                torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.8.attn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.8.ssm_norm.weight,                torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.8.ssm_out.weight,                 torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.8.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.8.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.8.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.8.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.9.attn_norm.weight,               torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:blk.9.ssm_a,                          torch.float32 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.9.ssm_conv1d.weight,              torch.bfloat16 --> F32, shape = {4, 6144}
+INFO:hf-to-gguf:blk.9.ssm_dt.bias,                    torch.bfloat16 --> F32, shape = {16}
+INFO:hf-to-gguf:blk.9.ssm_alpha.weight,               torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.9.ssm_beta.weight,                torch.bfloat16 --> BF16, shape = {1024, 16}
+INFO:hf-to-gguf:blk.9.attn_qkv.weight,                torch.bfloat16 --> BF16, shape = {1024, 6144}
+INFO:hf-to-gguf:blk.9.attn_gate.weight,               torch.bfloat16 --> BF16, shape = {1024, 2048}
+INFO:hf-to-gguf:blk.9.ssm_norm.weight,                torch.float32 --> F32, shape = {128}
+INFO:hf-to-gguf:blk.9.ssm_out.weight,                 torch.bfloat16 --> BF16, shape = {2048, 1024}
+INFO:hf-to-gguf:blk.9.ffn_down.weight,                torch.bfloat16 --> BF16, shape = {3584, 1024}
+INFO:hf-to-gguf:blk.9.ffn_gate.weight,                torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.9.ffn_up.weight,                  torch.bfloat16 --> BF16, shape = {1024, 3584}
+INFO:hf-to-gguf:blk.9.post_attention_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:output_norm.weight,                   torch.bfloat16 --> F32, shape = {1024}
+INFO:hf-to-gguf:Set meta model
+INFO:hf-to-gguf:Set model parameters
+INFO:hf-to-gguf:gguf: context length = 262144
+INFO:hf-to-gguf:gguf: embedding length = 1024
+INFO:hf-to-gguf:gguf: feed forward length = 3584
+INFO:hf-to-gguf:gguf: head count = 8
+INFO:hf-to-gguf:gguf: key-value head count = 2
+WARNING:hf-to-gguf:Unknown RoPE type: default
+INFO:hf-to-gguf:gguf: rope scaling type = NONE
+INFO:hf-to-gguf:gguf: mrope sections: [11, 11, 10, 0]
+INFO:hf-to-gguf:gguf: rope theta = 10000000
+INFO:hf-to-gguf:gguf: rms norm epsilon = 1e-06
+INFO:hf-to-gguf:gguf: file type = 32
+INFO:hf-to-gguf:Set model quantization version
+INFO:hf-to-gguf:Set model tokenizer
+WARNING:hf-to-gguf:
+
+WARNING:hf-to-gguf:**************************************************************************************
+WARNING:hf-to-gguf:** WARNING: The BPE pre-tokenizer was not recognized!
+WARNING:hf-to-gguf:**          There are 2 possible reasons for this:
+WARNING:hf-to-gguf:**          - the model has not been added to convert_hf_to_gguf_update.py yet
+WARNING:hf-to-gguf:**          - the pre-tokenization config has changed upstream
+WARNING:hf-to-gguf:**          Check your model files and convert_hf_to_gguf_update.py and update them accordingly.
+WARNING:hf-to-gguf:** ref:     https://github.com/ggml-org/llama.cpp/pull/6920
+WARNING:hf-to-gguf:**
+WARNING:hf-to-gguf:** chkhsh:  4f53cda18c2baa0c0354bb5f9a3ecbe5ed12ab4d8e11ba873c2f11161202b945
+WARNING:hf-to-gguf:**************************************************************************************
+WARNING:hf-to-gguf:
+
+Traceback (most recent call last):
+  File "/tmp/echo-llama-src/convert_hf_to_gguf.py", line 307, in <module>
+    main()
+    ~~~~^^
+  File "/tmp/echo-llama-src/convert_hf_to_gguf.py", line 301, in main
+    model_instance.write()
+    ~~~~~~~~~~~~~~~~~~~~^^
+  File "/tmp/echo-llama-src/conversion/base.py", line 1069, in write
+    self.prepare_metadata(vocab_only=False)
+    ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^
+  File "/tmp/echo-llama-src/conversion/qwen.py", line 358, in prepare_metadata
+    super().prepare_metadata(vocab_only=vocab_only)  # ty: ignore[unresolved-attribute]
+    ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
+  File "/tmp/echo-llama-src/conversion/base.py", line 1266, in prepare_metadata
+    self.set_vocab()
+    ~~~~~~~~~~~~~~^^
+  File "/tmp/echo-llama-src/conversion/base.py", line 1238, in set_vocab
+    self._set_vocab_gpt2()
+    ~~~~~~~~~~~~~~~~~~~~^^
+  File "/tmp/echo-llama-src/conversion/base.py", line 1787, in _set_vocab_gpt2
+    tokens, toktypes, tokpre = self.get_vocab_base()
+                               ~~~~~~~~~~~~~~~~~~~^^
+  File "/tmp/echo-llama-src/conversion/base.py", line 1424, in get_vocab_base
+    tokpre = self.get_vocab_base_pre(tokenizer)
+  File "/tmp/echo-llama-src/conversion/base.py", line 1775, in get_vocab_base_pre
+    raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
+NotImplementedError: BPE pre-tokenizer was not recognized - update get_vocab_base_pre()

MVP/evidence/student-q4-vulkan-forward.log

@@ -0,0 +1,5 @@
+=== COMMAND ===
+curl -sS http://127.0.0.1:60911/completion -H Content-Type:application/json -d {\"prompt\":\"2+2=\",\"n_predict\":1,\"temperature\":0}
+=== OUTPUT ===
+{"index":0,"content":"őd","tokens":[],"id_slot":0,"stop":true,"model":"MVP/artifacts/qwen35-text-n4-q4_k_m.gguf","tokens_predicted":1,"tokens_evaluated":4,"generation_settings":{"seed":4294967295,"temperature":0.0,"dynatemp_range":0.0,"dynatemp_exponent":1.0,"top_k":40,"top_p":0.949999988079071,"min_p":0.05000000074505806,"top_n_sigma":-1.0,"xtc_probability":0.0,"xtc_threshold":0.10000000149011612,"typical_p":1.0,"repeat_last_n":64,"repeat_penalty":1.0,"presence_penalty":0.0,"frequency_penalty":0.0,"dry_multiplier":0.0,"dry_base":1.75,"dry_allowed_length":2,"dry_penalty_last_n":256,"dry_sequence_breakers":["\n",":","\"","*"],"mirostat":0,"mirostat_tau":5.0,"mirostat_eta":0.10000000149011612,"stop":[],"max_tokens":1,"n_predict":1,"n_keep":0,"n_discard":0,"ignore_eos":false,"stream":false,"logit_bias":[],"n_probs":0,"min_keep":0,"grammar":"","grammar_lazy":false,"grammar_triggers":[],"preserved_tokens":[],"chat_format":"Content-only","reasoning_format":"deepseek","reasoning_in_content":false,"generation_prompt":"","samplers":["penalties","dry","top_n_sigma","top_k","typ_p","top_p","min_p","xtc","temperature"],"speculative.types":"none","timings_per_token":false,"post_sampling_probs":false,"backend_sampling":false,"lora":[]},"prompt":"2+2=","has_new_line":false,"truncated":false,"stop_type":"limit","stopping_word":"","tokens_cached":4,"timings":{"cache_n":0,"prompt_n":4,"prompt_ms":15.959,"prompt_per_token_ms":3.98975,"prompt_per_second":250.64227081897363,"predicted_n":1,"predicted_ms":0.001,"predicted_per_token_ms":0.001,"predicted_per_second":1000000.0}}
+HTTP/CURL_EXIT=0

MVP/evidence/student-q4-vulkan-no-host.log

@@ -0,0 +1,810 @@
+0.00.329.616 I cmn  common_param: common_params_print_info: build 10068 (571d0d540d) with GNU 16.1.1 for Linux x86_64 (debug)
+0.00.329.619 I cmn  common_param: common_params_print_info: verbosity = 2147483647 (adjust with the `-lv N` CLI arg)
+0.00.329.619 I cmn  common_param: device_info:
+0.00.329.717 I cmn  common_param:   - Vulkan0 : AMD Radeon RX 460 Graphics (RADV POLARIS11) (2048 MiB, 1483 MiB free)
+0.00.329.722 I cmn  common_param:   - CPU     : AMD Ryzen 5 3600 6-Core Processor (15914 MiB, 15914 MiB free)
+0.00.329.741 I cmn  common_param: system_info: n_threads = 6 (n_threads_batch = 6) / 12 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | OPENMP = 1 | REPACK = 1 | 
+0.00.329.777 I srv          init: running without SSL
+0.00.329.841 I srv          init: using 11 threads for HTTP server
+0.00.329.843 I srv          init: The UI is disabled
+0.00.329.844 I srv          init: Use --ui/--no-ui (or deprecated --webui/--no-webui) to enable/disable
+0.00.329.957 W srv  llama_server: -----------------
+0.00.329.960 W srv  llama_server: CORS is set to allow all origins ('*') and no API key is set
+0.00.329.960 W srv  llama_server: this can be a security risk (cross-origin attacks)
+0.00.329.960 W srv  llama_server: more info: https://github.com/ggml-org/llama.cpp/pull/25655
+0.00.329.961 W srv  llama_server: -----------------
+0.00.329.984 I srv         start: binding port with default address family
+0.00.331.221 I srv    load_model: loading model 'MVP/artifacts/qwen35-text-n4-q4_k_m.gguf'
+0.00.331.224 I srv    load_model: local path 'MVP/artifacts/qwen35-text-n4-q4_k_m.gguf'
+0.00.331.234 I cmn  common_init_: fitting params to device memory ...
+0.00.331.235 I cmn  common_init_: (for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on)
+0.00.331.236 I common_params_fit_impl: getting device memory data for initial parameters:
+0.00.385.259 I llama_model_loader: loaded meta data with 35 key-value pairs and 55 tensors from MVP/artifacts/qwen35-text-n4-q4_k_m.gguf (version GGUF V3 (latest))
+0.00.385.271 I llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
+0.00.385.273 I llama_model_loader: - kv   0:                       general.architecture str              = qwen35
+0.00.385.274 I llama_model_loader: - kv   1:                               general.type str              = model
+0.00.385.275 I llama_model_loader: - kv   2:                               general.name str              = Qwen35 0.8b Text N4
+0.00.385.276 I llama_model_loader: - kv   3:                           general.finetune str              = 0.8b-text-n4
+0.00.385.276 I llama_model_loader: - kv   4:                           general.basename str              = qwen35
+0.00.385.277 I llama_model_loader: - kv   5:                         general.size_label str              = 337M
+0.00.385.278 I llama_model_loader: - kv   6:                         qwen35.block_count u32              = 4
+0.00.385.279 I llama_model_loader: - kv   7:                      qwen35.context_length u32              = 262144
+0.00.385.279 I llama_model_loader: - kv   8:                    qwen35.embedding_length u32              = 1024
+0.00.385.280 I llama_model_loader: - kv   9:                 qwen35.feed_forward_length u32              = 3584
+0.00.385.281 I llama_model_loader: - kv  10:                qwen35.attention.head_count u32              = 8
+0.00.385.282 I llama_model_loader: - kv  11:             qwen35.attention.head_count_kv u32              = 2
+0.00.385.288 I llama_model_loader: - kv  12:             qwen35.rope.dimension_sections arr[i32,4]       = [11, 11, 10, 0]
+0.00.385.294 I llama_model_loader: - kv  13:                      qwen35.rope.freq_base f32              = 10000000.000000
+0.00.385.296 I llama_model_loader: - kv  14:    qwen35.attention.layer_norm_rms_epsilon f32              = 0.000001
+0.00.385.296 I llama_model_loader: - kv  15:                qwen35.attention.key_length u32              = 256
+0.00.385.297 I llama_model_loader: - kv  16:              qwen35.attention.value_length u32              = 256
+0.00.385.298 I llama_model_loader: - kv  17:                     qwen35.ssm.conv_kernel u32              = 4
+0.00.385.298 I llama_model_loader: - kv  18:                      qwen35.ssm.state_size u32              = 128
+0.00.385.299 I llama_model_loader: - kv  19:                     qwen35.ssm.group_count u32              = 16
+0.00.385.299 I llama_model_loader: - kv  20:                  qwen35.ssm.time_step_rank u32              = 16
+0.00.385.300 I llama_model_loader: - kv  21:                      qwen35.ssm.inner_size u32              = 2048
+0.00.385.300 I llama_model_loader: - kv  22:             qwen35.full_attention_interval u32              = 4
+0.00.385.301 I llama_model_loader: - kv  23:                qwen35.rope.dimension_count u32              = 64
+0.00.385.302 I llama_model_loader: - kv  24:                       tokenizer.ggml.model str              = gpt2
+0.00.385.302 I llama_model_loader: - kv  25:                         tokenizer.ggml.pre str              = qwen35
+0.00.410.944 I llama_model_loader: - kv  26:                      tokenizer.ggml.tokens arr[str,248320]  = ["!", "\"", "#", "$", "%", "&", "'", ...
+0.00.417.169 I llama_model_loader: - kv  27:                  tokenizer.ggml.token_type arr[i32,248320]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
+0.00.440.742 I llama_model_loader: - kv  28:                      tokenizer.ggml.merges arr[str,247587]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
+0.00.440.748 I llama_model_loader: - kv  29:                tokenizer.ggml.eos_token_id u32              = 248044
+0.00.440.749 I llama_model_loader: - kv  30:            tokenizer.ggml.padding_token_id u32              = 248044
+0.00.440.750 I llama_model_loader: - kv  31:               tokenizer.ggml.add_bos_token bool             = false
+0.00.440.754 I llama_model_loader: - kv  32:                    tokenizer.chat_template str              = {%- set image_count = namespace(value...
+0.00.440.755 I llama_model_loader: - kv  33:               general.quantization_version u32              = 2
+0.00.440.756 I llama_model_loader: - kv  34:                          general.file_type u32              = 15
+0.00.440.756 I llama_model_loader: - type  f32:   23 tensors
+0.00.440.757 I llama_model_loader: - type q4_K:   27 tensors
+0.00.440.757 I llama_model_loader: - type q6_K:    5 tensors
+0.00.440.759 I print_info: file format = GGUF V3 (latest)
+0.00.440.759 I print_info: file type   = Q4_K - Medium
+0.00.440.764 I print_info: file size   = 247.22 MiB (6.15 BPW) 
+0.00.440.904 I llama_prepare_model_devices: using device Vulkan0 (AMD Radeon RX 460 Graphics (RADV POLARIS11)) (0000:29:00.0) - 1483 MiB free
+0.00.572.724 D init_tokenizer: initializing tokenizer for type 2
+0.00.607.430 I load: 0 unused tokens
+0.00.607.444 D load: control token: 248075 '<tts_text_bos_single>' is not marked as EOG
+0.00.607.444 D load: control token: 248073 '<tts_text_bos>' is not marked as EOG
+0.00.607.445 D load: control token: 248072 '<tts_pad>' is not marked as EOG
+0.00.607.445 D load: control token: 248071 '<|audio_end|>' is not marked as EOG
+0.00.607.446 D load: control token: 248061 '<|fim_middle|>' is not marked as EOG
+0.00.607.446 D load: control token: 248055 '<|vision_pad|>' is not marked as EOG
+0.00.607.447 D load: control token: 248052 '<|quad_end|>' is not marked as EOG
+0.00.607.447 D load: control token: 248049 '<|box_start|>' is not marked as EOG
+0.00.607.447 D load: control token: 248048 '<|object_ref_end|>' is not marked as EOG
+0.00.607.448 D load: control token: 248045 '<|im_start|>' is not marked as EOG
+0.00.607.826 D load: control token: 248057 '<|video_pad|>' is not marked as EOG
+0.00.609.743 D load: control token: 248070 '<|audio_start|>' is not marked as EOG
+0.00.609.847 D load: control token: 248056 '<|image_pad|>' is not marked as EOG
+0.00.610.059 D load: control token: 248054 '<|vision_end|>' is not marked as EOG
+0.00.613.472 D load: control token: 248060 '<|fim_prefix|>' is not marked as EOG
+0.00.613.990 D load: control token: 248050 '<|box_end|>' is not marked as EOG
+0.00.619.932 D load: control token: 248074 '<tts_text_eod>' is not marked as EOG
+0.00.628.487 D load: control token: 248053 '<|vision_start|>' is not marked as EOG
+0.00.630.608 D load: control token: 248062 '<|fim_suffix|>' is not marked as EOG
+0.00.632.186 D load: control token: 248047 '<|object_ref_start|>' is not marked as EOG
+0.00.633.421 D load: control token: 248051 '<|quad_start|>' is not marked as EOG
+0.00.635.256 D load: control token: 248076 '<|audio_pad|>' is not marked as EOG
+0.00.652.740 I load: printing all EOG tokens:
+0.00.652.745 I load:   - 248044 ('<|endoftext|>')
+0.00.652.746 I load:   - 248046 ('<|im_end|>')
+0.00.652.746 I load:   - 248063 ('<|fim_pad|>')
+0.00.652.746 I load:   - 248064 ('<|repo_name|>')
+0.00.652.747 I load:   - 248065 ('<|file_sep|>')
+0.00.653.356 I load: special tokens cache size = 33
+0.00.736.530 I load: token to piece cache size = 1.7581 MB
+0.00.736.540 I print_info: arch                  = qwen35
+0.00.736.542 I print_info: vocab_only            = 0
+0.00.736.542 I print_info: no_alloc              = 1
+0.00.736.542 I print_info: n_ctx_train           = 262144
+0.00.736.544 I print_info: n_embd_inp            = 1024
+0.00.736.544 I print_info: n_embd                = 1024
+0.00.736.545 I print_info: n_embd_out            = 1024
+0.00.736.545 I print_info: n_layer               = 4
+0.00.736.545 I print_info: n_layer_all           = 4
+0.00.736.551 I print_info: n_head                = 8
+0.00.736.552 I print_info: n_head_kv             = 2
+0.00.736.552 I print_info: n_rot                 = 64
+0.00.736.552 I print_info: n_swa                 = 0
+0.00.736.553 I print_info: is_swa_any            = 0
+0.00.736.553 I print_info: n_embd_head_k         = 256
+0.00.736.553 I print_info: n_embd_head_v         = 256
+0.00.736.554 I print_info: n_gqa                 = 4
+0.00.736.554 I print_info: n_embd_k_gqa          = 512
+0.00.736.555 I print_info: n_embd_v_gqa          = 512
+0.00.736.555 I print_info: f_norm_eps            = 0.0e+00
+0.00.736.557 I print_info: f_norm_rms_eps        = 1.0e-06
+0.00.736.557 I print_info: f_clamp_kqv           = 0.0e+00
+0.00.736.557 I print_info: f_max_alibi_bias      = 0.0e+00
+0.00.736.557 I print_info: f_logit_scale         = 0.0e+00
+0.00.736.558 I print_info: f_attn_scale          = 0.0e+00
+0.00.736.558 I print_info: f_attn_value_scale    = 0.0000
+0.00.736.559 I print_info: n_ff                  = 3584
+0.00.736.559 I print_info: n_expert              = 0
+0.00.736.559 I print_info: n_expert_used         = 0
+0.00.736.572 I print_info: n_expert_groups       = 0
+0.00.736.573 I print_info: n_group_used          = 0
+0.00.736.573 I print_info: causal attn           = 1
+0.00.736.574 I print_info: pooling type          = -1
+0.00.736.574 I print_info: rope type             = 40
+0.00.736.574 I print_info: rope scaling          = linear
+0.00.736.576 I print_info: freq_base_train       = 10000000.0
+0.00.736.576 I print_info: freq_scale_train      = 1
+0.00.736.577 I print_info: n_ctx_orig_yarn       = 262144
+0.00.736.577 I print_info: rope_yarn_log_mul     = 0.0000
+0.00.736.577 I print_info: rope_finetuned        = unknown
+0.00.736.578 I print_info: mrope sections        = [11, 11, 10, 0]
+0.00.736.578 I print_info: ssm_d_conv            = 4
+0.00.736.578 I print_info: ssm_d_inner           = 2048
+0.00.736.578 I print_info: ssm_d_state           = 128
+0.00.736.579 I print_info: ssm_dt_rank           = 16
+0.00.736.579 I print_info: ssm_n_group           = 16
+0.00.736.579 I print_info: ssm_dt_b_c_rms        = 0
+0.00.736.580 I print_info: model type            = ?B
+0.00.736.580 I print_info: model params          = 337.30 M
+0.00.736.581 I print_info: general.name          = Qwen35 0.8b Text N4
+0.00.736.581 I print_info: vocab type            = BPE
+0.00.736.582 I print_info: n_vocab               = 248320
+0.00.736.582 I print_info: n_merges              = 247587
+0.00.736.582 I print_info: BOS token             = 11 ','
+0.00.736.583 I print_info: EOS token             = 248044 '<|endoftext|>'
+0.00.736.583 I print_info: EOT token             = 248046 '<|im_end|>'
+0.00.736.583 I print_info: PAD token             = 248044 '<|endoftext|>'
+0.00.736.583 I print_info: LF token              = 198 'Ċ'
+0.00.736.584 I print_info: FIM PRE token         = 248060 '<|fim_prefix|>'
+0.00.736.584 I print_info: FIM SUF token         = 248062 '<|fim_suffix|>'
+0.00.736.584 I print_info: FIM MID token         = 248061 '<|fim_middle|>'
+0.00.736.584 I print_info: FIM PAD token         = 248063 '<|fim_pad|>'
+0.00.736.584 I print_info: FIM REP token         = 248064 '<|repo_name|>'
+0.00.736.585 I print_info: FIM SEP token         = 248065 '<|file_sep|>'
+0.00.736.585 I print_info: EOG token             = 248044 '<|endoftext|>'
+0.00.736.586 I print_info: EOG token             = 248046 '<|im_end|>'
+0.00.736.586 I print_info: EOG token             = 248063 '<|fim_pad|>'
+0.00.736.586 I print_info: EOG token             = 248064 '<|repo_name|>'
+0.00.736.586 I print_info: EOG token             = 248065 '<|file_sep|>'
+0.00.736.587 I print_info: max token length      = 256
+0.00.736.587 I load_tensors: loading model tensors, this can take a while... (mmap = false, direct_io = false)
+0.00.743.323 D load_tensors: layer   0 assigned to device Vulkan0, is_swa = 0
+0.00.743.326 D load_tensors: layer   1 assigned to device Vulkan0, is_swa = 0
+0.00.743.326 D load_tensors: layer   2 assigned to device Vulkan0, is_swa = 0
+0.00.743.327 D load_tensors: layer   3 assigned to device Vulkan0, is_swa = 0
+0.00.743.327 D load_tensors: layer   4 assigned to device Vulkan0, is_swa = 0
+0.00.743.363 D create_tensor: loading tensor token_embd.weight
+0.00.743.372 D create_tensor: loading tensor output_norm.weight
+0.00.743.384 D create_tensor: loading tensor token_embd.weight
+0.00.743.389 D create_tensor: loading tensor blk.0.attn_norm.weight
+0.00.743.395 D create_tensor: loading tensor blk.0.post_attention_norm.weight
+0.00.743.399 D create_tensor: loading tensor blk.0.attn_qkv.weight
+0.00.743.411 D create_tensor: loading tensor blk.0.attn_gate.weight
+0.00.743.419 D create_tensor: loading tensor blk.0.ssm_conv1d.weight
+0.00.743.424 D create_tensor: loading tensor blk.0.ssm_dt.bias
+0.00.743.434 D create_tensor: loading tensor blk.0.ssm_a
+0.00.743.446 D create_tensor: loading tensor blk.0.ssm_beta.weight
+0.00.743.461 D create_tensor: loading tensor blk.0.ssm_alpha.weight
+0.00.743.470 D create_tensor: loading tensor blk.0.ssm_norm.weight
+0.00.743.486 D create_tensor: loading tensor blk.0.ssm_out.weight
+0.00.743.514 D create_tensor: loading tensor blk.0.ffn_gate.weight
+0.00.743.537 D create_tensor: loading tensor blk.0.ffn_down.weight
+0.00.743.545 D create_tensor: loading tensor blk.0.ffn_up.weight
+0.00.743.551 D create_tensor: loading tensor blk.1.attn_norm.weight
+0.00.743.557 D create_tensor: loading tensor blk.1.post_attention_norm.weight
+0.00.743.563 D create_tensor: loading tensor blk.1.attn_qkv.weight
+0.00.743.570 D create_tensor: loading tensor blk.1.attn_gate.weight
+0.00.743.576 D create_tensor: loading tensor blk.1.ssm_conv1d.weight
+0.00.743.583 D create_tensor: loading tensor blk.1.ssm_dt.bias
+0.00.743.588 D create_tensor: loading tensor blk.1.ssm_a
+0.00.743.595 D create_tensor: loading tensor blk.1.ssm_beta.weight
+0.00.743.601 D create_tensor: loading tensor blk.1.ssm_alpha.weight
+0.00.743.607 D create_tensor: loading tensor blk.1.ssm_norm.weight
+0.00.743.611 D create_tensor: loading tensor blk.1.ssm_out.weight
+0.00.743.615 D create_tensor: loading tensor blk.1.ffn_gate.weight
+0.00.743.622 D create_tensor: loading tensor blk.1.ffn_down.weight
+0.00.743.627 D create_tensor: loading tensor blk.1.ffn_up.weight
+0.00.743.632 D create_tensor: loading tensor blk.2.attn_norm.weight
+0.00.743.641 D create_tensor: loading tensor blk.2.post_attention_norm.weight
+0.00.743.648 D create_tensor: loading tensor blk.2.attn_qkv.weight
+0.00.743.653 D create_tensor: loading tensor blk.2.attn_gate.weight
+0.00.743.657 D create_tensor: loading tensor blk.2.ssm_conv1d.weight
+0.00.743.663 D create_tensor: loading tensor blk.2.ssm_dt.bias
+0.00.743.667 D create_tensor: loading tensor blk.2.ssm_a
+0.00.743.672 D create_tensor: loading tensor blk.2.ssm_beta.weight
+0.00.743.677 D create_tensor: loading tensor blk.2.ssm_alpha.weight
+0.00.743.684 D create_tensor: loading tensor blk.2.ssm_norm.weight
+0.00.743.689 D create_tensor: loading tensor blk.2.ssm_out.weight
+0.00.743.694 D create_tensor: loading tensor blk.2.ffn_gate.weight
+0.00.743.698 D create_tensor: loading tensor blk.2.ffn_down.weight
+0.00.743.703 D create_tensor: loading tensor blk.2.ffn_up.weight
+0.00.743.709 D create_tensor: loading tensor blk.3.attn_norm.weight
+0.00.743.715 D create_tensor: loading tensor blk.3.post_attention_norm.weight
+0.00.743.725 D create_tensor: loading tensor blk.3.attn_q.weight
+0.00.743.731 D create_tensor: loading tensor blk.3.attn_k.weight
+0.00.743.736 D create_tensor: loading tensor blk.3.attn_v.weight
+0.00.743.747 D create_tensor: loading tensor blk.3.attn_output.weight
+0.00.743.755 D create_tensor: loading tensor blk.3.attn_q_norm.weight
+0.00.743.760 D create_tensor: loading tensor blk.3.attn_k_norm.weight
+0.00.743.765 D create_tensor: loading tensor blk.3.ffn_gate.weight
+0.00.743.771 D create_tensor: loading tensor blk.3.ffn_down.weight
+0.00.743.776 D create_tensor: loading tensor blk.3.ffn_up.weight
+0.00.743.878 D done_getting_tensors: tensor 'token_embd.weight' (q6_K) (and 0 others) cannot be used with preferred buffer type CPU_REPACK, using CPU instead
+0.00.743.966 I load_tensors: offloading output layer to GPU
+0.00.743.972 I load_tensors: offloading 3 repeating layers to GPU
+0.00.743.973 I load_tensors: offloaded 5/5 layers to GPU
+0.00.743.977 I load_tensors:          CPU model buffer size =     0.00 MiB
+0.00.743.978 I load_tensors:      Vulkan0 model buffer size =     0.00 MiB
+0.00.747.472 I llama_context: constructing llama_context
+0.00.747.476 I llama_context: n_seq_max     = 1
+0.00.747.476 I llama_context: n_ctx         = 256
+0.00.747.476 I llama_context: n_ctx_seq     = 256
+0.00.747.476 I llama_context: n_batch       = 64
+0.00.747.476 I llama_context: n_ubatch      = 1
+0.00.747.477 I llama_context: causal_attn   = 1
+0.00.747.477 I llama_context: flash_attn    = auto
+0.00.747.477 I llama_context: kv_unified    = false
+0.00.747.482 I llama_context: freq_base     = 10000000.0
+0.00.747.482 I llama_context: freq_scale    = 1
+0.00.747.482 I llama_context: n_rs_seq      = 0
+0.00.747.483 I llama_context: n_outputs_max = 1
+0.00.747.483 I llama_context: n_ctx_seq (256) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
+0.00.747.501 D set_abort_callback: call
+0.00.747.737 I llama_context: Vulkan_Host  output buffer size =     0.95 MiB
+0.00.747.751 D llama_kv_cache: layer   0: filtered
+0.00.747.754 D llama_kv_cache: layer   1: filtered
+0.00.747.754 D llama_kv_cache: layer   2: filtered
+0.00.747.756 D llama_kv_cache: layer   3: dev = Vulkan0
+0.00.747.766 I llama_kv_cache:    Vulkan0 KV buffer size =     0.00 MiB
+0.00.747.767 I llama_kv_cache: size =    0.50 MiB (   256 cells,   1 layers,  1/1 seqs), K (f16):    0.25 MiB, V (f16):    0.25 MiB
+0.00.747.768 I llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
+0.00.747.768 I llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
+0.00.747.770 D llama_memory_recurrent, layer   0: dev = Vulkan0
+0.00.747.772 D llama_memory_recurrent, layer   1: dev = Vulkan0
+0.00.747.772 D llama_memory_recurrent, layer   2: dev = Vulkan0
+0.00.747.773 D llama_memory_recurrent: layer   3: skipped
+0.00.749.752 I llama_memory_recurrent:    Vulkan0 RS buffer size =     3.21 MiB
+0.00.749.756 I llama_memory_recurrent: size =    3.21 MiB (     1 cells,   4 layers,  1 seqs  0 rs_seq), R (f32):    0.21 MiB, S (f32):    3.00 MiB
+0.00.749.757 D llama_context: enumerating backends
+0.00.749.759 D llama_context: backend_ptrs.size() = 2
+0.00.749.759 I sched_reserve: reserving ...
+0.00.749.760 D sched_reserve: max_nodes = 1792
+0.00.749.953 D sched_reserve: reserving full memory module
+0.00.749.956 D sched_reserve: worst-case: n_tokens = 1, n_seqs = 1, n_outputs = 1
+0.00.749.957 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.750.231 I resolve_fused_ops: Flash Attention enabled
+0.00.750.233 I resolve_fused_ops: resolving fused Gated Delta Net support:
+0.00.750.233 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.750.425 I resolve_fused_ops: fused Gated Delta Net (autoregressive) enabled
+0.00.750.427 D graph_reserve: reserving a graph for ubatch with n_tokens =   16, n_seqs =  1, n_outputs =   16
+0.00.750.610 I resolve_fused_ops: fused Gated Delta Net (chunked) enabled
+0.00.750.611 I resolve_fused_ops: resolving fused Lightning Indexer support:
+0.00.750.612 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.750.794 I resolve_fused_ops: Lightning Indexer enabled
+0.00.750.796 I resolve_fused_ops: resolving fused DeepSeek V4 HC support:
+0.00.750.796 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.750.976 I resolve_fused_ops: fused DeepSeek V4 HC pre enabled
+0.00.750.977 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.751.157 I resolve_fused_ops: fused DeepSeek V4 HC comb enabled
+0.00.751.159 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.751.332 I resolve_fused_ops: fused DeepSeek V4 HC post enabled
+0.00.751.333 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.751.571 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.751.751 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.751.930 I sched_reserve:    Vulkan0 compute buffer size =     2.12 MiB
+0.00.751.932 I sched_reserve: Vulkan_Host compute buffer size =     0.04 MiB
+0.00.751.932 I sched_reserve: graph nodes  = 235
+0.00.751.933 I sched_reserve: graph splits = 2
+0.00.751.933 I sched_reserve: reserve took 2.17 ms, sched copies = 1
+0.00.752.171 I common_memory_breakdown_print: | memory breakdown [MiB]                         | total   free    self   model   context   compute    unaccounted |
+0.00.752.173 I common_memory_breakdown_print: |   - Vulkan0 (RX 460 Graphics (RADV POLARIS11)) |  2048 = 1479 + ( 253 =   247 +       3 +       2) +         315 |
+0.00.752.173 I common_memory_breakdown_print: |   - Host                                       |                  198 =   198 +       0 +       0                |
+0.00.787.280 I common_params_fit_impl: projected to use 253 MiB of device memory vs. 1479 MiB of free device memory
+0.00.787.283 I common_params_fit_impl: will leave 1226 >= 1024 MiB of free device memory, no changes needed
+0.00.787.284 I common_fit_params: successfully fit params to free device memory
+0.00.787.288 I common_fit_params: fitting params to free memory took 0.46 seconds
+0.00.822.645 I llama_model_loader: loaded meta data with 35 key-value pairs and 55 tensors from MVP/artifacts/qwen35-text-n4-q4_k_m.gguf (version GGUF V3 (latest))
+0.00.822.653 I llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
+0.00.822.656 I llama_model_loader: - kv   0:                       general.architecture str              = qwen35
+0.00.822.656 I llama_model_loader: - kv   1:                               general.type str              = model
+0.00.822.657 I llama_model_loader: - kv   2:                               general.name str              = Qwen35 0.8b Text N4
+0.00.822.657 I llama_model_loader: - kv   3:                           general.finetune str              = 0.8b-text-n4
+0.00.822.658 I llama_model_loader: - kv   4:                           general.basename str              = qwen35
+0.00.822.658 I llama_model_loader: - kv   5:                         general.size_label str              = 337M
+0.00.822.659 I llama_model_loader: - kv   6:                         qwen35.block_count u32              = 4
+0.00.822.660 I llama_model_loader: - kv   7:                      qwen35.context_length u32              = 262144
+0.00.822.660 I llama_model_loader: - kv   8:                    qwen35.embedding_length u32              = 1024
+0.00.822.661 I llama_model_loader: - kv   9:                 qwen35.feed_forward_length u32              = 3584
+0.00.822.661 I llama_model_loader: - kv  10:                qwen35.attention.head_count u32              = 8
+0.00.822.662 I llama_model_loader: - kv  11:             qwen35.attention.head_count_kv u32              = 2
+0.00.822.668 I llama_model_loader: - kv  12:             qwen35.rope.dimension_sections arr[i32,4]       = [11, 11, 10, 0]
+0.00.822.674 I llama_model_loader: - kv  13:                      qwen35.rope.freq_base f32              = 10000000.000000
+0.00.822.675 I llama_model_loader: - kv  14:    qwen35.attention.layer_norm_rms_epsilon f32              = 0.000001
+0.00.822.676 I llama_model_loader: - kv  15:                qwen35.attention.key_length u32              = 256
+0.00.822.676 I llama_model_loader: - kv  16:              qwen35.attention.value_length u32              = 256
+0.00.822.677 I llama_model_loader: - kv  17:                     qwen35.ssm.conv_kernel u32              = 4
+0.00.822.677 I llama_model_loader: - kv  18:                      qwen35.ssm.state_size u32              = 128
+0.00.822.678 I llama_model_loader: - kv  19:                     qwen35.ssm.group_count u32              = 16
+0.00.822.678 I llama_model_loader: - kv  20:                  qwen35.ssm.time_step_rank u32              = 16
+0.00.822.679 I llama_model_loader: - kv  21:                      qwen35.ssm.inner_size u32              = 2048
+0.00.822.680 I llama_model_loader: - kv  22:             qwen35.full_attention_interval u32              = 4
+0.00.822.680 I llama_model_loader: - kv  23:                qwen35.rope.dimension_count u32              = 64
+0.00.822.681 I llama_model_loader: - kv  24:                       tokenizer.ggml.model str              = gpt2
+0.00.822.681 I llama_model_loader: - kv  25:                         tokenizer.ggml.pre str              = qwen35
+0.00.845.042 I llama_model_loader: - kv  26:                      tokenizer.ggml.tokens arr[str,248320]  = ["!", "\"", "#", "$", "%", "&", "'", ...
+0.00.851.463 I llama_model_loader: - kv  27:                  tokenizer.ggml.token_type arr[i32,248320]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
+0.00.873.809 I llama_model_loader: - kv  28:                      tokenizer.ggml.merges arr[str,247587]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
+0.00.873.815 I llama_model_loader: - kv  29:                tokenizer.ggml.eos_token_id u32              = 248044
+0.00.873.815 I llama_model_loader: - kv  30:            tokenizer.ggml.padding_token_id u32              = 248044
+0.00.873.816 I llama_model_loader: - kv  31:               tokenizer.ggml.add_bos_token bool             = false
+0.00.873.819 I llama_model_loader: - kv  32:                    tokenizer.chat_template str              = {%- set image_count = namespace(value...
+0.00.873.820 I llama_model_loader: - kv  33:               general.quantization_version u32              = 2
+0.00.873.820 I llama_model_loader: - kv  34:                          general.file_type u32              = 15
+0.00.873.821 I llama_model_loader: - type  f32:   23 tensors
+0.00.873.822 I llama_model_loader: - type q4_K:   27 tensors
+0.00.873.822 I llama_model_loader: - type q6_K:    5 tensors
+0.00.873.823 I print_info: file format = GGUF V3 (latest)
+0.00.873.824 I print_info: file type   = Q4_K - Medium
+0.00.873.827 I print_info: file size   = 247.22 MiB (6.15 BPW) 
+0.00.873.946 I llama_prepare_model_devices: using device Vulkan0 (AMD Radeon RX 460 Graphics (RADV POLARIS11)) (0000:29:00.0) - 1482 MiB free
+0.00.992.953 D init_tokenizer: initializing tokenizer for type 2
+0.01.030.936 I load: 0 unused tokens
+0.01.030.949 D load: control token: 248075 '<tts_text_bos_single>' is not marked as EOG
+0.01.030.949 D load: control token: 248073 '<tts_text_bos>' is not marked as EOG
+0.01.030.950 D load: control token: 248072 '<tts_pad>' is not marked as EOG
+0.01.030.950 D load: control token: 248071 '<|audio_end|>' is not marked as EOG
+0.01.030.951 D load: control token: 248061 '<|fim_middle|>' is not marked as EOG
+0.01.030.951 D load: control token: 248055 '<|vision_pad|>' is not marked as EOG
+0.01.030.952 D load: control token: 248052 '<|quad_end|>' is not marked as EOG
+0.01.030.953 D load: control token: 248049 '<|box_start|>' is not marked as EOG
+0.01.030.953 D load: control token: 248048 '<|object_ref_end|>' is not marked as EOG
+0.01.030.954 D load: control token: 248045 '<|im_start|>' is not marked as EOG
+0.01.031.298 D load: control token: 248057 '<|video_pad|>' is not marked as EOG
+0.01.033.250 D load: control token: 248070 '<|audio_start|>' is not marked as EOG
+0.01.033.356 D load: control token: 248056 '<|image_pad|>' is not marked as EOG
+0.01.033.588 D load: control token: 248054 '<|vision_end|>' is not marked as EOG
+0.01.037.951 D load: control token: 248060 '<|fim_prefix|>' is not marked as EOG
+0.01.038.537 D load: control token: 248050 '<|box_end|>' is not marked as EOG
+0.01.044.521 D load: control token: 248074 '<tts_text_eod>' is not marked as EOG
+0.01.053.306 D load: control token: 248053 '<|vision_start|>' is not marked as EOG
+0.01.055.951 D load: control token: 248062 '<|fim_suffix|>' is not marked as EOG
+0.01.057.620 D load: control token: 248047 '<|object_ref_start|>' is not marked as EOG
+0.01.058.885 D load: control token: 248051 '<|quad_start|>' is not marked as EOG
+0.01.060.478 D load: control token: 248076 '<|audio_pad|>' is not marked as EOG
+0.01.081.476 I load: printing all EOG tokens:
+0.01.081.481 I load:   - 248044 ('<|endoftext|>')
+0.01.081.481 I load:   - 248046 ('<|im_end|>')
+0.01.081.482 I load:   - 248063 ('<|fim_pad|>')
+0.01.081.482 I load:   - 248064 ('<|repo_name|>')
+0.01.081.482 I load:   - 248065 ('<|file_sep|>')
+0.01.082.152 I load: special tokens cache size = 33
+0.01.175.956 I load: token to piece cache size = 1.7581 MB
+0.01.175.968 I print_info: arch                  = qwen35
+0.01.175.968 I print_info: vocab_only            = 0
+0.01.175.969 I print_info: no_alloc              = 0
+0.01.175.969 I print_info: n_ctx_train           = 262144
+0.01.175.969 I print_info: n_embd_inp            = 1024
+0.01.175.969 I print_info: n_embd                = 1024
+0.01.175.970 I print_info: n_embd_out            = 1024
+0.01.175.970 I print_info: n_layer               = 4
+0.01.175.970 I print_info: n_layer_all           = 4
+0.01.175.977 I print_info: n_head                = 8
+0.01.175.977 I print_info: n_head_kv             = 2
+0.01.175.978 I print_info: n_rot                 = 64
+0.01.175.978 I print_info: n_swa                 = 0
+0.01.175.978 I print_info: is_swa_any            = 0
+0.01.175.978 I print_info: n_embd_head_k         = 256
+0.01.175.979 I print_info: n_embd_head_v         = 256
+0.01.175.979 I print_info: n_gqa                 = 4
+0.01.175.980 I print_info: n_embd_k_gqa          = 512
+0.01.175.981 I print_info: n_embd_v_gqa          = 512
+0.01.175.981 I print_info: f_norm_eps            = 0.0e+00
+0.01.175.982 I print_info: f_norm_rms_eps        = 1.0e-06
+0.01.175.982 I print_info: f_clamp_kqv           = 0.0e+00
+0.01.175.983 I print_info: f_max_alibi_bias      = 0.0e+00
+0.01.175.983 I print_info: f_logit_scale         = 0.0e+00
+0.01.175.983 I print_info: f_attn_scale          = 0.0e+00
+0.01.175.984 I print_info: f_attn_value_scale    = 0.0000
+0.01.175.984 I print_info: n_ff                  = 3584
+0.01.175.984 I print_info: n_expert              = 0
+0.01.175.984 I print_info: n_expert_used         = 0
+0.01.175.985 I print_info: n_expert_groups       = 0
+0.01.175.985 I print_info: n_group_used          = 0
+0.01.175.985 I print_info: causal attn           = 1
+0.01.175.985 I print_info: pooling type          = -1
+0.01.175.985 I print_info: rope type             = 40
+0.01.175.985 I print_info: rope scaling          = linear
+0.01.175.987 I print_info: freq_base_train       = 10000000.0
+0.01.175.987 I print_info: freq_scale_train      = 1
+0.01.175.987 I print_info: n_ctx_orig_yarn       = 262144
+0.01.175.988 I print_info: rope_yarn_log_mul     = 0.0000
+0.01.175.989 I print_info: rope_finetuned        = unknown
+0.01.175.989 I print_info: mrope sections        = [11, 11, 10, 0]
+0.01.175.989 I print_info: ssm_d_conv            = 4
+0.01.175.990 I print_info: ssm_d_inner           = 2048
+0.01.175.990 I print_info: ssm_d_state           = 128
+0.01.175.991 I print_info: ssm_dt_rank           = 16
+0.01.175.991 I print_info: ssm_n_group           = 16
+0.01.175.991 I print_info: ssm_dt_b_c_rms        = 0
+0.01.175.992 I print_info: model type            = ?B
+0.01.175.993 I print_info: model params          = 337.30 M
+0.01.175.993 I print_info: general.name          = Qwen35 0.8b Text N4
+0.01.175.994 I print_info: vocab type            = BPE
+0.01.175.995 I print_info: n_vocab               = 248320
+0.01.175.995 I print_info: n_merges              = 247587
+0.01.175.996 I print_info: BOS token             = 11 ','
+0.01.175.996 I print_info: EOS token             = 248044 '<|endoftext|>'
+0.01.175.996 I print_info: EOT token             = 248046 '<|im_end|>'
+0.01.175.997 I print_info: PAD token             = 248044 '<|endoftext|>'
+0.01.175.997 I print_info: LF token              = 198 'Ċ'
+0.01.175.998 I print_info: FIM PRE token         = 248060 '<|fim_prefix|>'
+0.01.175.998 I print_info: FIM SUF token         = 248062 '<|fim_suffix|>'
+0.01.175.998 I print_info: FIM MID token         = 248061 '<|fim_middle|>'
+0.01.175.998 I print_info: FIM PAD token         = 248063 '<|fim_pad|>'
+0.01.175.999 I print_info: FIM REP token         = 248064 '<|repo_name|>'
+0.01.175.999 I print_info: FIM SEP token         = 248065 '<|file_sep|>'
+0.01.175.999 I print_info: EOG token             = 248044 '<|endoftext|>'
+0.01.176.000 I print_info: EOG token             = 248046 '<|im_end|>'
+0.01.176.000 I print_info: EOG token             = 248063 '<|fim_pad|>'
+0.01.176.001 I print_info: EOG token             = 248064 '<|repo_name|>'
+0.01.176.001 I print_info: EOG token             = 248065 '<|file_sep|>'
+0.01.176.001 I print_info: max token length      = 256
+0.01.176.002 I load_tensors: loading model tensors, this can take a while... (mmap = true, direct_io = false)
+0.01.176.165 D load_tensors: layer   0 assigned to device Vulkan0, is_swa = 0
+0.01.176.167 D load_tensors: layer   1 assigned to device Vulkan0, is_swa = 0
+0.01.176.168 D load_tensors: layer   2 assigned to device Vulkan0, is_swa = 0
+0.01.176.168 D load_tensors: layer   3 assigned to device Vulkan0, is_swa = 0
+0.01.176.169 D load_tensors: layer   4 assigned to device Vulkan0, is_swa = 0
+0.01.176.185 D create_tensor: loading tensor token_embd.weight
+0.01.176.192 D create_tensor: loading tensor output_norm.weight
+0.01.176.205 D create_tensor: loading tensor token_embd.weight
+0.01.176.212 D create_tensor: loading tensor blk.0.attn_norm.weight
+0.01.176.218 D create_tensor: loading tensor blk.0.post_attention_norm.weight
+0.01.176.225 D create_tensor: loading tensor blk.0.attn_qkv.weight
+0.01.176.231 D create_tensor: loading tensor blk.0.attn_gate.weight
+0.01.176.238 D create_tensor: loading tensor blk.0.ssm_conv1d.weight
+0.01.176.243 D create_tensor: loading tensor blk.0.ssm_dt.bias
+0.01.176.250 D create_tensor: loading tensor blk.0.ssm_a
+0.01.176.257 D create_tensor: loading tensor blk.0.ssm_beta.weight
+0.01.176.263 D create_tensor: loading tensor blk.0.ssm_alpha.weight
+0.01.176.268 D create_tensor: loading tensor blk.0.ssm_norm.weight
+0.01.176.274 D create_tensor: loading tensor blk.0.ssm_out.weight
+0.01.176.279 D create_tensor: loading tensor blk.0.ffn_gate.weight
+0.01.176.285 D create_tensor: loading tensor blk.0.ffn_down.weight
+0.01.176.290 D create_tensor: loading tensor blk.0.ffn_up.weight
+0.01.176.299 D create_tensor: loading tensor blk.1.attn_norm.weight
+0.01.176.305 D create_tensor: loading tensor blk.1.post_attention_norm.weight
+0.01.176.312 D create_tensor: loading tensor blk.1.attn_qkv.weight
+0.01.176.317 D create_tensor: loading tensor blk.1.attn_gate.weight
+0.01.176.325 D create_tensor: loading tensor blk.1.ssm_conv1d.weight
+0.01.176.330 D create_tensor: loading tensor blk.1.ssm_dt.bias
+0.01.176.337 D create_tensor: loading tensor blk.1.ssm_a
+0.01.176.343 D create_tensor: loading tensor blk.1.ssm_beta.weight
+0.01.176.349 D create_tensor: loading tensor blk.1.ssm_alpha.weight
+0.01.176.355 D create_tensor: loading tensor blk.1.ssm_norm.weight
+0.01.176.362 D create_tensor: loading tensor blk.1.ssm_out.weight
+0.01.176.368 D create_tensor: loading tensor blk.1.ffn_gate.weight
+0.01.176.374 D create_tensor: loading tensor blk.1.ffn_down.weight
+0.01.176.380 D create_tensor: loading tensor blk.1.ffn_up.weight
+0.01.176.387 D create_tensor: loading tensor blk.2.attn_norm.weight
+0.01.176.393 D create_tensor: loading tensor blk.2.post_attention_norm.weight
+0.01.176.400 D create_tensor: loading tensor blk.2.attn_qkv.weight
+0.01.176.406 D create_tensor: loading tensor blk.2.attn_gate.weight
+0.01.176.413 D create_tensor: loading tensor blk.2.ssm_conv1d.weight
+0.01.176.419 D create_tensor: loading tensor blk.2.ssm_dt.bias
+0.01.176.425 D create_tensor: loading tensor blk.2.ssm_a
+0.01.176.431 D create_tensor: loading tensor blk.2.ssm_beta.weight
+0.01.176.438 D create_tensor: loading tensor blk.2.ssm_alpha.weight
+0.01.176.444 D create_tensor: loading tensor blk.2.ssm_norm.weight
+0.01.176.449 D create_tensor: loading tensor blk.2.ssm_out.weight
+0.01.176.455 D create_tensor: loading tensor blk.2.ffn_gate.weight
+0.01.176.461 D create_tensor: loading tensor blk.2.ffn_down.weight
+0.01.176.468 D create_tensor: loading tensor blk.2.ffn_up.weight
+0.01.176.475 D create_tensor: loading tensor blk.3.attn_norm.weight
+0.01.176.483 D create_tensor: loading tensor blk.3.post_attention_norm.weight
+0.01.176.492 D create_tensor: loading tensor blk.3.attn_q.weight
+0.01.176.499 D create_tensor: loading tensor blk.3.attn_k.weight
+0.01.176.506 D create_tensor: loading tensor blk.3.attn_v.weight
+0.01.176.520 D create_tensor: loading tensor blk.3.attn_output.weight
+0.01.176.528 D create_tensor: loading tensor blk.3.attn_q_norm.weight
+0.01.176.535 D create_tensor: loading tensor blk.3.attn_k_norm.weight
+0.01.176.540 D create_tensor: loading tensor blk.3.ffn_gate.weight
+0.01.176.546 D create_tensor: loading tensor blk.3.ffn_down.weight
+0.01.176.553 D create_tensor: loading tensor blk.3.ffn_up.weight
+0.01.176.654 D done_getting_tensors: tensor 'token_embd.weight' (q6_K) (and 0 others) cannot be used with preferred buffer type CPU_REPACK, using CPU instead
+0.01.303.166 I load_tensors: offloading output layer to GPU
+0.01.303.172 I load_tensors: offloading 3 repeating layers to GPU
+0.01.303.173 I load_tensors: offloaded 5/5 layers to GPU
+0.01.303.178 I load_tensors:   CPU_Mapped model buffer size =   198.93 MiB
+0.01.303.179 I load_tensors:      Vulkan0 model buffer size =   247.22 MiB
+0.01.387.760 I cmn  common_init_: added <|endoftext|> logit bias = -inf
+0.01.387.763 I cmn  common_init_: added <|im_end|> logit bias = -inf
+0.01.387.764 I cmn  common_init_: added <|fim_pad|> logit bias = -inf
+0.01.387.764 I cmn  common_init_: added <|repo_name|> logit bias = -inf
+0.01.387.764 I cmn  common_init_: added <|file_sep|> logit bias = -inf
+0.01.387.804 I llama_context: constructing llama_context
+0.01.387.810 I llama_context: n_seq_max     = 1
+0.01.387.810 I llama_context: n_ctx         = 256
+0.01.387.810 I llama_context: n_ctx_seq     = 256
+0.01.387.810 I llama_context: n_batch       = 64
+0.01.387.810 I llama_context: n_ubatch      = 1
+0.01.387.811 I llama_context: causal_attn   = 1
+0.01.387.812 I llama_context: flash_attn    = auto
+0.01.387.812 I llama_context: kv_unified    = false
+0.01.387.815 I llama_context: freq_base     = 10000000.0
+0.01.387.816 I llama_context: freq_scale    = 1
+0.01.387.816 I llama_context: n_rs_seq      = 0
+0.01.387.817 I llama_context: n_outputs_max = 1
+0.01.387.817 I llama_context: n_ctx_seq (256) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
+0.01.387.839 D set_abort_callback: call
+0.01.388.178 I llama_context: Vulkan_Host  output buffer size =     0.95 MiB
+0.01.388.189 D llama_kv_cache: layer   0: filtered
+0.01.388.189 D llama_kv_cache: layer   1: filtered
+0.01.388.190 D llama_kv_cache: layer   2: filtered
+0.01.388.191 D llama_kv_cache: layer   3: dev = Vulkan0
+0.01.388.237 I llama_kv_cache:    Vulkan0 KV buffer size =     0.50 MiB
+0.01.388.406 I llama_kv_cache: size =    0.50 MiB (   256 cells,   1 layers,  1/1 seqs), K (f16):    0.25 MiB, V (f16):    0.25 MiB
+0.01.388.409 I llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
+0.01.388.409 I llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
+0.01.388.412 D llama_memory_recurrent, layer   0: dev = Vulkan0
+0.01.388.413 D llama_memory_recurrent, layer   1: dev = Vulkan0
+0.01.388.414 D llama_memory_recurrent, layer   2: dev = Vulkan0
+0.01.388.414 D llama_memory_recurrent: layer   3: skipped
+0.01.388.776 I llama_memory_recurrent:    Vulkan0 RS buffer size =     3.21 MiB
+0.01.388.780 I llama_memory_recurrent: size =    3.21 MiB (     1 cells,   4 layers,  1 seqs  0 rs_seq), R (f32):    0.21 MiB, S (f32):    3.00 MiB
+0.01.388.781 D llama_context: enumerating backends
+0.01.388.783 D llama_context: backend_ptrs.size() = 2
+0.01.388.783 I sched_reserve: reserving ...
+0.01.388.784 D sched_reserve: max_nodes = 1792
+0.01.389.010 D sched_reserve: reserving full memory module
+0.01.389.014 D sched_reserve: worst-case: n_tokens = 1, n_seqs = 1, n_outputs = 1
+0.01.389.014 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.389.303 I resolve_fused_ops: Flash Attention enabled
+0.01.389.304 I resolve_fused_ops: resolving fused Gated Delta Net support:
+0.01.389.305 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.389.543 I resolve_fused_ops: fused Gated Delta Net (autoregressive) enabled
+0.01.389.544 D graph_reserve: reserving a graph for ubatch with n_tokens =   16, n_seqs =  1, n_outputs =   16
+0.01.389.743 I resolve_fused_ops: fused Gated Delta Net (chunked) enabled
+0.01.389.745 I resolve_fused_ops: resolving fused Lightning Indexer support:
+0.01.389.746 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.389.946 I resolve_fused_ops: Lightning Indexer enabled
+0.01.389.948 I resolve_fused_ops: resolving fused DeepSeek V4 HC support:
+0.01.389.949 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.390.161 I resolve_fused_ops: fused DeepSeek V4 HC pre enabled
+0.01.390.164 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.390.394 I resolve_fused_ops: fused DeepSeek V4 HC comb enabled
+0.01.390.396 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.390.619 I resolve_fused_ops: fused DeepSeek V4 HC post enabled
+0.01.390.621 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.391.058 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.391.361 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.391.669 I sched_reserve:    Vulkan0 compute buffer size =     2.12 MiB
+0.01.391.673 I sched_reserve: Vulkan_Host compute buffer size =     0.04 MiB
+0.01.391.673 I sched_reserve: graph nodes  = 235
+0.01.391.674 I sched_reserve: graph splits = 2
+0.01.391.675 I sched_reserve: reserve took 2.89 ms, sched copies = 1
+0.01.391.824 D set_adapters_lora: adapters = (nil)
+0.01.391.826 D adapters_lora_are_same: adapters = (nil)
+0.01.391.827 I cmn  common_init_: warming up the model with an empty run - please wait ... (--no-warmup to disable)
+0.01.428.357 I cmn  common_conte: the context does not support partial sequence removal
+0.01.432.677 I srv    load_model: speculative decoding will use checkpoints
+0.01.432.680 I srv    load_model: initializing, n_slots = 1, n_ctx_slot = 256, kv_unified = 'false'
+0.01.432.682 I spec common_specu: no implementations specified for speculative decoding
+0.01.432.683 I slot   load_model: id  0 | task -1 | new slot, n_ctx = 256
+0.01.432.683 D slot        reset: id  0 | task -1 | 
+0.01.432.691 I srv    load_model: prompt cache is enabled, size limit: 8192 MiB
+0.01.432.691 I srv    load_model: use `--cache-ram 0` to disable the prompt cache
+0.01.432.692 I srv    load_model: for more info see https://github.com/ggml-org/llama.cpp/pull/16391
+0.01.432.692 I srv    load_model: context checkpoints enabled, max = 32, min spacing = 8192
+0.01.432.705 I srv          init: idle slots will be saved to prompt cache upon starting a new task
+0.01.432.705 D srv          init: __TEST_TAG_CACHE_IDLE_SLOTS_ENABLED__
+0.01.435.897 D common_chat_templates_apply_jinja: using differential autoparser
+0.01.435.900 D === Starting differential analysis ===
+0.01.435.901 D Phase 1: Reasoning analysis
+0.01.438.029 D Phase 2: Content analysis
+0.01.440.117 D Phase 3: Tool call analysis
+0.01.442.680 D Phase 3a: Function call analysis
+0.01.443.902 D Phase 3b: Argument analysis
+0.01.448.294 D Phase 3c: Call id analysis
+0.01.449.891 D Template application failed: 
+------------
+While executing CallExpression at line 43, column 24 in source:
+... not messages %}↵    {{- raise_exception('No messages provided.') }}↵{%- endif %...
+                                           ^
+Error: Jinja Exception: No messages provided.
+0.01.450.076 D detect_user_start_marker: Template application failed, unsupported empty messages? trying complex variant
+0.01.450.726 D 
+--- Reasoning & Content Structure ---
+0.01.450.727 D user_msg_start: <|im_start|>user
+0.01.450.728 D assistant_msg_start: <|im_start|>assistant
+0.01.450.728 D reasoning_mode: TAG_BASED
+0.01.450.728 D reasoning_start: '<think>
+'
+0.01.450.729 D reasoning_end: '
+</think>
+
+'
+0.01.450.729 D content_mode: PLAIN
+0.01.450.729 D content_start: ''
+0.01.450.729 D content_end: ''
+0.01.450.729 D 
+--- Tool Call Structure ---
+0.01.450.730 D tool_mode: TAG_WITH_TAGGED
+0.01.450.730 D supports_tools: true
+0.01.450.730 D supports_parallel_calls: true
+0.01.450.730 D tool_section_start: ''
+0.01.450.731 D tool_section_end: ''
+0.01.450.731 D per_call_start: '<tool_call>
+'
+0.01.450.731 D per_call_end: '</tool_call>'
+0.01.450.731 D func_name_prefix: '<function='
+0.01.450.731 D func_name_suffix: '>
+'
+0.01.450.731 D func_args_separator: ''
+0.01.450.731 D func_close: '</function>
+'
+0.01.450.732 D call_id_prefix: ''
+0.01.450.732 D call_id_suffix: ''
+0.01.450.732 D call_id_pos: 'NONE'
+0.01.450.732 D args_start: ''
+0.01.450.732 D args_end: ''
+0.01.450.732 D arg_name_prefix: '<parameter='
+0.01.450.733 D arg_name_suffix: '>
+'
+0.01.450.733 D arg_value_prefix: ''
+0.01.450.733 D arg_value_suffix: '
+</parameter>
+'
+0.01.450.733 D name_field: 'name'
+0.01.450.733 D args_field: 'arguments'
+0.01.450.733 D id_field: ''
+0.01.450.734 D gen_id_field: ''
+0.01.450.735 D parameter_order: ''
+0.01.450.735 D === Differential analysis complete ===
+0.01.452.115 D common_chat_templates_apply_jinja: generated parser:
+Sequence(Literal(<|im_start|>assistant
+), Space, Epsilon, Tag(content, Until()), End)
+
+parser generation prompt: <|im_start|>assistant
+<think>
+
+0.01.452.122 I srv          init: init: chat template, example_format: '<|im_start|>system
+You are a helpful assistant<|im_end|>
+<|im_start|>user
+Hello<|im_end|>
+<|im_start|>assistant
+Hi there<|im_end|>
+<|im_start|>user
+How are you?<|im_end|>
+<|im_start|>assistant
+<think>
+'
+0.01.452.162 D common_chat_templates_apply_jinja: using differential autoparser
+0.01.452.163 D === Starting differential analysis ===
+0.01.452.163 D Phase 1: Reasoning analysis
+0.01.454.006 D Phase 2: Content analysis
+0.01.455.625 D Phase 3: Tool call analysis
+0.01.457.717 D Phase 3a: Function call analysis
+0.01.458.763 D Phase 3b: Argument analysis
+0.01.462.837 D Phase 3c: Call id analysis
+0.01.464.368 D Template application failed: 
+------------
+While executing CallExpression at line 43, column 24 in source:
+... not messages %}↵    {{- raise_exception('No messages provided.') }}↵{%- endif %...
+                                           ^
+Error: Jinja Exception: No messages provided.
+0.01.464.541 D detect_user_start_marker: Template application failed, unsupported empty messages? trying complex variant
+0.01.465.152 D 
+--- Reasoning & Content Structure ---
+0.01.465.153 D user_msg_start: <|im_start|>user
+0.01.465.153 D assistant_msg_start: <|im_start|>assistant
+0.01.465.154 D reasoning_mode: TAG_BASED
+0.01.465.154 D reasoning_start: '<think>
+'
+0.01.465.154 D reasoning_end: '
+</think>
+
+'
+0.01.465.154 D content_mode: PLAIN
+0.01.465.155 D content_start: ''
+0.01.465.155 D content_end: ''
+0.01.465.155 D 
+--- Tool Call Structure ---
+0.01.465.155 D tool_mode: TAG_WITH_TAGGED
+0.01.465.155 D supports_tools: true
+0.01.465.156 D supports_parallel_calls: true
+0.01.465.156 D tool_section_start: ''
+0.01.465.156 D tool_section_end: ''
+0.01.465.156 D per_call_start: '<tool_call>
+'
+0.01.465.156 D per_call_end: '</tool_call>'
+0.01.465.156 D func_name_prefix: '<function='
+0.01.465.156 D func_name_suffix: '>
+'
+0.01.465.156 D func_args_separator: ''
+0.01.465.157 D func_close: '</function>
+'
+0.01.465.157 D call_id_prefix: ''
+0.01.465.157 D call_id_suffix: ''
+0.01.465.157 D call_id_pos: 'NONE'
+0.01.465.157 D args_start: ''
+0.01.465.157 D args_end: ''
+0.01.465.158 D arg_name_prefix: '<parameter='
+0.01.465.158 D arg_name_suffix: '>
+'
+0.01.465.158 D arg_value_prefix: ''
+0.01.465.158 D arg_value_suffix: '
+</parameter>
+'
+0.01.465.158 D name_field: 'name'
+0.01.465.158 D args_field: 'arguments'
+0.01.465.158 D id_field: ''
+0.01.465.158 D gen_id_field: ''
+0.01.465.159 D parameter_order: ''
+0.01.465.159 D === Differential analysis complete ===
+0.01.465.835 D common_chat_templates_apply_jinja: generated parser:
+Sequence(Literal(<|im_start|>assistant
+), Space, Repetition(Sequence(Epsilon, Literal(<think>), Repetition(Literal(
+), 0, 1), Tag(reasoning, Until(</think>)), Epsilon, Repetition(Literal(
+), 0, 1), Literal(</think>), Repetition(Literal(
+), 0, 1), Repetition(Literal(
+), 0, 1)), 0, 1), Tag(content, Until()), End)
+
+parser generation prompt: <|im_start|>assistant
+<think>
+
+0.01.465.840 I srv          init: init: chat template, thinking = 1
+0.01.465.853 I srv  llama_server: model loaded
+0.01.465.856 I srv  llama_server: listening on http://127.0.0.1:60911
+0.01.465.858 D que    start_loop: processing new tasks
+0.01.465.858 D que    start_loop: update slots
+0.01.465.860 I srv  update_slots: all slots are idle
+0.01.465.860 D que    start_loop: waiting for new tasks
+0.32.026.989 I srv  server_strea: conv_id= (empty=1)
+0.32.027.031 D srv  eval_llama_c: reasoning budget: tokens=-1, generation_prompt='', start=0 toks, end=0 toks, forced=0 toks
+0.32.027.069 D res  add_waiting_: add task 0 to waiting list. current waiting = 0 (before add)
+0.32.027.071 D que          post: new task, id = 0/1, front = 0
+0.32.027.087 D que    start_loop: processing new tasks
+0.32.027.091 D que    start_loop: processing task, id = 0
+0.32.027.094 I slot get_availabl: id  0 | task -1 | selected slot by LRU, t_last = -1
+0.32.027.094 I srv  get_availabl: updating prompt cache
+0.32.027.099 I srv          load:  - looking for better prompt, base f_keep = -1.000, sim = 0.000
+0.32.027.104 I srv        update:  - cache state: 0 prompts, 0.000 MiB (limits: 8192.000 MiB, 256 tokens, 8589934592 est)
+0.32.027.108 I srv  get_availabl: prompt cache update took 0.01 ms
+0.32.027.121 D slot launch_slot_: id  0 | task -1 | launching slot : {"id":0,"n_ctx":256,"speculative":false,"is_processing":false}
+0.32.027.139 I slot launch_slot_: id  0 | task -1 | sampler chain: logits -> ?penalties -> ?dry -> ?top-n-sigma -> top-k -> ?typical -> top-p -> min-p -> ?xtc -> temp-ext -> dist 
+0.32.027.145 I slot launch_slot_: id  0 | task -1 | sampler params: 
+	repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
+	dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 256
+	top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.000
+	mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000, adaptive_target = -1.000, adaptive_decay = 0.900
+0.32.027.147 I slot launch_slot_: id  0 | task 0 | processing task, is_child = 0
+0.32.027.148 D que    start_loop: update slots
+0.32.027.149 D srv  update_slots: posting NEXT_RESPONSE
+0.32.027.150 D que          post: new task, id = 1, front = 0
+0.32.027.154 I slot   operator(): id  0 | task 0 | new prompt, n_ctx_slot = 256, n_keep = 0, task.n_tokens = 4
+0.32.027.157 I slot   operator(): id  0 | task 0 | cached n_tokens = 0, memory_seq_rm [0, end)
+0.32.027.166 I slot init_sampler: id  0 | task 0 | init sampler, took 0.00 ms, tokens: text = 4, total = 4
+0.32.027.167 D slot   operator(): id  0 | task 0 | main/do_checkpoint = no, pos_min = -1, pos_max = -1
+0.32.027.169 D set_adapters_lora: adapters = (nil)
+0.32.027.170 D adapters_lora_are_same: adapters = (nil)
+0.32.027.170 D set_embeddings: value = 0
+0.32.027.171 D srv        decode: n_batch (effective) = 64, off = 0
+0.32.043.120 D slot process_toke: id  0 | task 0 | stopped by limit, n_decoded = 1, n_predict = 1
+0.32.043.123 D slot process_toke: id  0 | task 0 | n_decoded = 1, n_remaining = 0, next token: 156383 'őd'
+0.32.043.128 I slot print_timing: id  0 | task 0 | prompt eval time =      15.96 ms /     4 tokens (    3.99 ms per token,   250.64 tokens per second)
+0.32.043.129 I slot print_timing: id  0 | task 0 |        eval time =       0.00 ms /     1 tokens (    0.00 ms per token, 1000000.00 tokens per second)
+0.32.043.131 I slot print_timing: id  0 | task 0 |       total time =      15.96 ms /     5 tokens
+0.32.043.132 I slot print_timing: id  0 | task 0 |    graphs reused =          1
+0.32.043.138 D res          send: sending result for task id = 0
+0.32.043.138 D res          send: task id = 0 pushed to result queue
+0.32.043.141 I slot      release: id  0 | task 0 | stop processing: n_tokens = 4, truncated = 0
+0.32.043.141 D slot        reset: id  0 | task 0 | 
+0.32.043.145 D que    start_loop: waiting for new tasks
+0.32.043.145 D que    start_loop: processing new tasks
+0.32.043.148 D que    start_loop: processing task, id = 1
+0.32.043.149 D que    start_loop: update slots
+0.32.043.149 I srv  update_slots: all slots are idle
+0.32.043.150 D que    start_loop: waiting for new tasks
+0.32.043.158 D No parser definition detected, assuming pure content parser.0.32.043.175 D Parsed message: {"role":"assistant","content":"őd"}
+0.32.043.266 D srv          stop: all tasks already finished, no need to cancel
+0.32.043.271 D res  remove_waiti: remove task 0 from waiting list. current waiting = 1 (before remove)
+0.32.043.271 D srv          stop: all tasks already finished, no need to cancel

MVP/evidence/student-quantize-q4_k_m.log

@@ -0,0 +1,106 @@
+ggml_vulkan: Found 1 Vulkan devices:
+ggml_vulkan: 0 = AMD Radeon RX 460 Graphics (RADV POLARIS11) (radv) | uma: 0 | fp16: 0 | bf16: 0 | fp4: 0 | warp size: 64 | shared memory: 65536 | int dot: 0 | matrix cores: none
+load_backend: loaded Vulkan backend from /usr/lib/ggml/libggml-vulkan.so
+load_backend: loaded CPU backend from /usr/lib/ggml/libggml-cpu-haswell.so
+llama_print_build_info: build = 10068 (571d0d540d)
+llama_print_build_info: built with GNU 16.1.1 for Linux x86_64
+llama_quantize: quantizing 'MVP/artifacts/qwen35-text-n4-bf16.gguf' to 'MVP/artifacts/qwen35-text-n4-q4_k_m.gguf' as Q4_K_M
+llama_model_loader: loaded meta data with 35 key-value pairs and 55 tensors from MVP/artifacts/qwen35-text-n4-bf16.gguf (version GGUF V3 (latest))
+llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
+llama_model_loader: - kv   0:                       general.architecture str              = qwen35
+llama_model_loader: - kv   1:                               general.type str              = model
+llama_model_loader: - kv   2:                               general.name str              = Qwen35 0.8b Text N4
+llama_model_loader: - kv   3:                           general.finetune str              = 0.8b-text-n4
+llama_model_loader: - kv   4:                           general.basename str              = qwen35
+llama_model_loader: - kv   5:                         general.size_label str              = 337M
+llama_model_loader: - kv   6:                         qwen35.block_count u32              = 4
+llama_model_loader: - kv   7:                      qwen35.context_length u32              = 262144
+llama_model_loader: - kv   8:                    qwen35.embedding_length u32              = 1024
+llama_model_loader: - kv   9:                 qwen35.feed_forward_length u32              = 3584
+llama_model_loader: - kv  10:                qwen35.attention.head_count u32              = 8
+llama_model_loader: - kv  11:             qwen35.attention.head_count_kv u32              = 2
+llama_model_loader: - kv  12:             qwen35.rope.dimension_sections arr[i32,4]       = [11, 11, 10, 0]
+llama_model_loader: - kv  13:                      qwen35.rope.freq_base f32              = 10000000.000000
+llama_model_loader: - kv  14:    qwen35.attention.layer_norm_rms_epsilon f32              = 0.000001
+llama_model_loader: - kv  15:                qwen35.attention.key_length u32              = 256
+llama_model_loader: - kv  16:              qwen35.attention.value_length u32              = 256
+llama_model_loader: - kv  17:                          general.file_type u32              = 32
+llama_model_loader: - kv  18:                     qwen35.ssm.conv_kernel u32              = 4
+llama_model_loader: - kv  19:                      qwen35.ssm.state_size u32              = 128
+llama_model_loader: - kv  20:                     qwen35.ssm.group_count u32              = 16
+llama_model_loader: - kv  21:                  qwen35.ssm.time_step_rank u32              = 16
+llama_model_loader: - kv  22:                      qwen35.ssm.inner_size u32              = 2048
+llama_model_loader: - kv  23:             qwen35.full_attention_interval u32              = 4
+llama_model_loader: - kv  24:                qwen35.rope.dimension_count u32              = 64
+llama_model_loader: - kv  25:               general.quantization_version u32              = 2
+llama_model_loader: - kv  26:                       tokenizer.ggml.model str              = gpt2
+llama_model_loader: - kv  27:                         tokenizer.ggml.pre str              = qwen35
+llama_model_loader: - kv  28:                      tokenizer.ggml.tokens arr[str,248320]  = ["!", "\"", "#", "$", "%", "&", "'", ...
+llama_model_loader: - kv  29:                  tokenizer.ggml.token_type arr[i32,248320]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
+llama_model_loader: - kv  30:                      tokenizer.ggml.merges arr[str,247587]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
+llama_model_loader: - kv  31:                tokenizer.ggml.eos_token_id u32              = 248044
+llama_model_loader: - kv  32:            tokenizer.ggml.padding_token_id u32              = 248044
+llama_model_loader: - kv  33:               tokenizer.ggml.add_bos_token bool             = false
+llama_model_loader: - kv  34:                    tokenizer.chat_template str              = {%- set image_count = namespace(value...
+llama_model_loader: - type  f32:   23 tensors
+llama_model_loader: - type bf16:   32 tensors
+[   1/  55] output_norm.weight                   - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[   2/  55] token_embd.weight                    - [  1024, 248320,      1,      1], type =   bf16, converting to q6_K .. size =   485.00 MiB ->   198.93 MiB
+[   3/  55] blk.0.attn_gate.weight               - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[   4/  55] blk.0.attn_norm.weight               - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[   5/  55] blk.0.attn_qkv.weight                - [  1024,   6144,      1,      1], type =   bf16, converting to q4_K .. size =    12.00 MiB ->     3.38 MiB
+[   6/  55] blk.0.ffn_down.weight                - [  3584,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[   7/  55] blk.0.ffn_gate.weight                - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[   8/  55] blk.0.ffn_up.weight                  - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[   9/  55] blk.0.post_attention_norm.weight     - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  10/  55] blk.0.ssm_a                          - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  11/  55] blk.0.ssm_alpha.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  12/  55] blk.0.ssm_beta.weight                - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  13/  55] blk.0.ssm_conv1d.weight              - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[  14/  55] blk.0.ssm_dt.bias                    - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  15/  55] blk.0.ssm_norm.weight                - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  16/  55] blk.0.ssm_out.weight                 - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  17/  55] blk.1.attn_gate.weight               - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  18/  55] blk.1.attn_norm.weight               - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  19/  55] blk.1.attn_qkv.weight                - [  1024,   6144,      1,      1], type =   bf16, converting to q4_K .. size =    12.00 MiB ->     3.38 MiB
+[  20/  55] blk.1.ffn_down.weight                - [  3584,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  21/  55] blk.1.ffn_gate.weight                - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  22/  55] blk.1.ffn_up.weight                  - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  23/  55] blk.1.post_attention_norm.weight     - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  24/  55] blk.1.ssm_a                          - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  25/  55] blk.1.ssm_alpha.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  26/  55] blk.1.ssm_beta.weight                - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  27/  55] blk.1.ssm_conv1d.weight              - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[  28/  55] blk.1.ssm_dt.bias                    - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  29/  55] blk.1.ssm_norm.weight                - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  30/  55] blk.1.ssm_out.weight                 - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  31/  55] blk.2.attn_gate.weight               - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  32/  55] blk.2.attn_norm.weight               - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  33/  55] blk.2.attn_qkv.weight                - [  1024,   6144,      1,      1], type =   bf16, converting to q6_K .. size =    12.00 MiB ->     4.92 MiB
+[  34/  55] blk.2.ffn_down.weight                - [  3584,   1024,      1,      1], type =   bf16, converting to q6_K .. size =     7.00 MiB ->     2.87 MiB
+[  35/  55] blk.2.ffn_gate.weight                - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  36/  55] blk.2.ffn_up.weight                  - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  37/  55] blk.2.post_attention_norm.weight     - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  38/  55] blk.2.ssm_a                          - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  39/  55] blk.2.ssm_alpha.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  40/  55] blk.2.ssm_beta.weight                - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  41/  55] blk.2.ssm_conv1d.weight              - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[  42/  55] blk.2.ssm_dt.bias                    - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  43/  55] blk.2.ssm_norm.weight                - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  44/  55] blk.2.ssm_out.weight                 - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  45/  55] blk.3.attn_k.weight                  - [  1024,    512,      1,      1], type =   bf16, converting to q4_K .. size =     1.00 MiB ->     0.28 MiB
+[  46/  55] blk.3.attn_k_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
+[  47/  55] blk.3.attn_norm.weight               - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  48/  55] blk.3.attn_output.weight             - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  49/  55] blk.3.attn_q.weight                  - [  1024,   4096,      1,      1], type =   bf16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB
+[  50/  55] blk.3.attn_q_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
+[  51/  55] blk.3.attn_v.weight                  - [  1024,    512,      1,      1], type =   bf16, converting to q6_K .. size =     1.00 MiB ->     0.41 MiB
+[  52/  55] blk.3.ffn_down.weight                - [  3584,   1024,      1,      1], type =   bf16, converting to q6_K .. size =     7.00 MiB ->     2.87 MiB
+[  53/  55] blk.3.ffn_gate.weight                - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  54/  55] blk.3.ffn_up.weight                  - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  55/  55] blk.3.post_attention_norm.weight     - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+llama_model_quantize_impl: model size  =   643.51 MiB (16.00 BPW)
+llama_model_quantize_impl: quant size  =   247.22 MiB (6.15 BPW)
+
+llama_quantize: quantize time =  3899.07 ms
+llama_quantize:    total time =  3899.07 ms

MVP/evidence/student-vulkan-inference-b128.log

@@ -0,0 +1,37 @@
+
+
+Loading model... 
+
+▄▄ ▄▄
+██ ██
+██ ██  ▀▀█▄ ███▄███▄  ▀▀█▄    ▄████ ████▄ ████▄
+██ ██ ▄█▀██ ██ ██ ██ ▄█▀██    ██    ██ ██ ██ ██
+██ ██ ▀█▄██ ██ ██ ██ ▀█▄██ ██ ▀████ ████▀ ████▀
+                                    ██    ██
+                                    ▀▀    ▀▀
+
+build      : b10068-571d0d540d
+model      : MVP/artifacts/qwen35-text-n4-bf16.gguf
+ftype      : BF16
+modalities : text
+
+available commands:
+  /exit or Ctrl+C     stop or exit
+  /regen              regenerate the last response
+  /clear              clear the chat history
+  /read <file>        add a text file
+  /glob <pattern>     add text files using globbing pattern
+
+
+
+> Kısa cevap ver: 2+2 kaçtır?
+
+[Start thinking]
+
+osuessoressorẽablishablish大业大业
+
+[ Prompt: 201.3 t/s | Generation: 100.7 t/s ]
+
+> 
+
+Exiting...

MVP/evidence/student-vulkan-inference-verbose.log

@@ -0,0 +1,753 @@
+0.00.138.038 I load_backend: loaded Vulkan backend from /usr/lib/ggml/libggml-vulkan.so
+0.00.170.142 I load_backend: loaded CPU backend from /usr/lib/ggml/libggml-cpu-haswell.so
+0.00.170.673 I cmn  common_param: common_params_print_info: build 10068 (571d0d540d) with GNU 16.1.1 for Linux x86_64 (debug)
+0.00.170.677 I cmn  common_param: common_params_print_info: verbosity = 2147483647 (adjust with the `-lv N` CLI arg)
+0.00.170.677 I cmn  common_param: device_info:
+0.00.170.898 I cmn  common_param:   - Vulkan0 : AMD Radeon RX 460 Graphics (RADV POLARIS11) (2048 MiB, 1453 MiB free)
+0.00.170.907 I cmn  common_param:   - CPU     : AMD Ryzen 5 3600 6-Core Processor (15914 MiB, 15914 MiB free)
+0.00.170.945 I cmn  common_param: system_info: n_threads = 1 (n_threads_batch = 1) / 12 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | OPENMP = 1 | REPACK = 1 | 
+0.00.170.987 I srv          init: running without SSL
+0.00.171.048 I srv          init: using 11 threads for HTTP server
+0.00.171.082 D srv          init: serve nocache for _app/version.json
+0.00.171.339 D srv          init: serve nocache for build.json
+0.00.171.350 D srv          init: serve nocache for manifest.webmanifest
+0.00.171.393 D srv          init: serve nocache for sw.js
+0.00.171.519 W srv  llama_server: -----------------
+0.00.171.520 W srv  llama_server: CORS is set to allow all origins ('*') and no API key is set
+0.00.171.520 W srv  llama_server: this can be a security risk (cross-origin attacks)
+0.00.171.520 W srv  llama_server: more info: https://github.com/ggml-org/llama.cpp/pull/25655
+0.00.171.521 W srv  llama_server: -----------------
+0.00.171.529 I srv         start: binding port with default address family
+0.00.172.748 I srv    load_model: loading model 'MVP/artifacts/qwen35-text-n4-bf16.gguf'
+0.00.172.750 I srv    load_model: local path 'MVP/artifacts/qwen35-text-n4-bf16.gguf'
+0.00.220.916 I llama_model_loader: loaded meta data with 35 key-value pairs and 55 tensors from MVP/artifacts/qwen35-text-n4-bf16.gguf (version GGUF V3 (latest))
+0.00.220.929 I llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
+0.00.220.932 I llama_model_loader: - kv   0:                       general.architecture str              = qwen35
+0.00.220.933 I llama_model_loader: - kv   1:                               general.type str              = model
+0.00.220.934 I llama_model_loader: - kv   2:                               general.name str              = Qwen35 0.8b Text N4
+0.00.220.935 I llama_model_loader: - kv   3:                           general.finetune str              = 0.8b-text-n4
+0.00.220.935 I llama_model_loader: - kv   4:                           general.basename str              = qwen35
+0.00.220.936 I llama_model_loader: - kv   5:                         general.size_label str              = 337M
+0.00.220.937 I llama_model_loader: - kv   6:                         qwen35.block_count u32              = 4
+0.00.220.938 I llama_model_loader: - kv   7:                      qwen35.context_length u32              = 262144
+0.00.220.938 I llama_model_loader: - kv   8:                    qwen35.embedding_length u32              = 1024
+0.00.220.939 I llama_model_loader: - kv   9:                 qwen35.feed_forward_length u32              = 3584
+0.00.220.939 I llama_model_loader: - kv  10:                qwen35.attention.head_count u32              = 8
+0.00.220.940 I llama_model_loader: - kv  11:             qwen35.attention.head_count_kv u32              = 2
+0.00.220.947 I llama_model_loader: - kv  12:             qwen35.rope.dimension_sections arr[i32,4]       = [11, 11, 10, 0]
+0.00.220.953 I llama_model_loader: - kv  13:                      qwen35.rope.freq_base f32              = 10000000.000000
+0.00.220.955 I llama_model_loader: - kv  14:    qwen35.attention.layer_norm_rms_epsilon f32              = 0.000001
+0.00.220.955 I llama_model_loader: - kv  15:                qwen35.attention.key_length u32              = 256
+0.00.220.956 I llama_model_loader: - kv  16:              qwen35.attention.value_length u32              = 256
+0.00.220.957 I llama_model_loader: - kv  17:                          general.file_type u32              = 32
+0.00.220.957 I llama_model_loader: - kv  18:                     qwen35.ssm.conv_kernel u32              = 4
+0.00.220.958 I llama_model_loader: - kv  19:                      qwen35.ssm.state_size u32              = 128
+0.00.220.959 I llama_model_loader: - kv  20:                     qwen35.ssm.group_count u32              = 16
+0.00.220.959 I llama_model_loader: - kv  21:                  qwen35.ssm.time_step_rank u32              = 16
+0.00.220.960 I llama_model_loader: - kv  22:                      qwen35.ssm.inner_size u32              = 2048
+0.00.220.960 I llama_model_loader: - kv  23:             qwen35.full_attention_interval u32              = 4
+0.00.220.961 I llama_model_loader: - kv  24:                qwen35.rope.dimension_count u32              = 64
+0.00.220.962 I llama_model_loader: - kv  25:               general.quantization_version u32              = 2
+0.00.220.962 I llama_model_loader: - kv  26:                       tokenizer.ggml.model str              = gpt2
+0.00.220.963 I llama_model_loader: - kv  27:                         tokenizer.ggml.pre str              = qwen35
+0.00.251.182 I llama_model_loader: - kv  28:                      tokenizer.ggml.tokens arr[str,248320]  = ["!", "\"", "#", "$", "%", "&", "'", ...
+0.00.257.772 I llama_model_loader: - kv  29:                  tokenizer.ggml.token_type arr[i32,248320]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
+0.00.279.979 I llama_model_loader: - kv  30:                      tokenizer.ggml.merges arr[str,247587]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
+0.00.279.986 I llama_model_loader: - kv  31:                tokenizer.ggml.eos_token_id u32              = 248044
+0.00.279.987 I llama_model_loader: - kv  32:            tokenizer.ggml.padding_token_id u32              = 248044
+0.00.279.988 I llama_model_loader: - kv  33:               tokenizer.ggml.add_bos_token bool             = false
+0.00.279.990 I llama_model_loader: - kv  34:                    tokenizer.chat_template str              = {%- set image_count = namespace(value...
+0.00.279.991 I llama_model_loader: - type  f32:   23 tensors
+0.00.279.993 I llama_model_loader: - type bf16:   32 tensors
+0.00.279.995 I print_info: file format = GGUF V3 (latest)
+0.00.279.996 I print_info: file type   = BF16
+0.00.280.000 I print_info: file size   = 643.51 MiB (16.00 BPW) 
+0.00.280.144 I llama_prepare_model_devices: using device Vulkan0 (AMD Radeon RX 460 Graphics (RADV POLARIS11)) (0000:29:00.0) - 1453 MiB free
+0.00.430.883 D init_tokenizer: initializing tokenizer for type 2
+0.00.470.983 I load: 0 unused tokens
+0.00.470.997 D load: control token: 248075 '<tts_text_bos_single>' is not marked as EOG
+0.00.470.998 D load: control token: 248073 '<tts_text_bos>' is not marked as EOG
+0.00.470.998 D load: control token: 248072 '<tts_pad>' is not marked as EOG
+0.00.470.998 D load: control token: 248071 '<|audio_end|>' is not marked as EOG
+0.00.470.999 D load: control token: 248061 '<|fim_middle|>' is not marked as EOG
+0.00.471.000 D load: control token: 248055 '<|vision_pad|>' is not marked as EOG
+0.00.471.000 D load: control token: 248052 '<|quad_end|>' is not marked as EOG
+0.00.471.000 D load: control token: 248049 '<|box_start|>' is not marked as EOG
+0.00.471.000 D load: control token: 248048 '<|object_ref_end|>' is not marked as EOG
+0.00.471.002 D load: control token: 248045 '<|im_start|>' is not marked as EOG
+0.00.471.373 D load: control token: 248057 '<|video_pad|>' is not marked as EOG
+0.00.473.298 D load: control token: 248070 '<|audio_start|>' is not marked as EOG
+0.00.473.410 D load: control token: 248056 '<|image_pad|>' is not marked as EOG
+0.00.473.652 D load: control token: 248054 '<|vision_end|>' is not marked as EOG
+0.00.477.671 D load: control token: 248060 '<|fim_prefix|>' is not marked as EOG
+0.00.478.263 D load: control token: 248050 '<|box_end|>' is not marked as EOG
+0.00.484.501 D load: control token: 248074 '<tts_text_eod>' is not marked as EOG
+0.00.493.554 D load: control token: 248053 '<|vision_start|>' is not marked as EOG
+0.00.496.017 D load: control token: 248062 '<|fim_suffix|>' is not marked as EOG
+0.00.497.867 D load: control token: 248047 '<|object_ref_start|>' is not marked as EOG
+0.00.499.231 D load: control token: 248051 '<|quad_start|>' is not marked as EOG
+0.00.501.064 D load: control token: 248076 '<|audio_pad|>' is not marked as EOG
+0.00.521.478 I load: printing all EOG tokens:
+0.00.521.482 I load:   - 248044 ('<|endoftext|>')
+0.00.521.482 I load:   - 248046 ('<|im_end|>')
+0.00.521.483 I load:   - 248063 ('<|fim_pad|>')
+0.00.521.483 I load:   - 248064 ('<|repo_name|>')
+0.00.521.483 I load:   - 248065 ('<|file_sep|>')
+0.00.521.984 I load: special tokens cache size = 33
+0.00.608.184 I load: token to piece cache size = 1.7581 MB
+0.00.608.194 I print_info: arch                  = qwen35
+0.00.608.195 I print_info: vocab_only            = 0
+0.00.608.196 I print_info: no_alloc              = 0
+0.00.608.196 I print_info: n_ctx_train           = 262144
+0.00.608.196 I print_info: n_embd_inp            = 1024
+0.00.608.197 I print_info: n_embd                = 1024
+0.00.608.197 I print_info: n_embd_out            = 1024
+0.00.608.197 I print_info: n_layer               = 4
+0.00.608.197 I print_info: n_layer_all           = 4
+0.00.608.203 I print_info: n_head                = 8
+0.00.608.204 I print_info: n_head_kv             = 2
+0.00.608.204 I print_info: n_rot                 = 64
+0.00.608.205 I print_info: n_swa                 = 0
+0.00.608.205 I print_info: is_swa_any            = 0
+0.00.608.205 I print_info: n_embd_head_k         = 256
+0.00.608.205 I print_info: n_embd_head_v         = 256
+0.00.608.206 I print_info: n_gqa                 = 4
+0.00.608.207 I print_info: n_embd_k_gqa          = 512
+0.00.608.207 I print_info: n_embd_v_gqa          = 512
+0.00.608.208 I print_info: f_norm_eps            = 0.0e+00
+0.00.608.209 I print_info: f_norm_rms_eps        = 1.0e-06
+0.00.608.209 I print_info: f_clamp_kqv           = 0.0e+00
+0.00.608.209 I print_info: f_max_alibi_bias      = 0.0e+00
+0.00.608.210 I print_info: f_logit_scale         = 0.0e+00
+0.00.608.210 I print_info: f_attn_scale          = 0.0e+00
+0.00.608.210 I print_info: f_attn_value_scale    = 0.0000
+0.00.608.211 I print_info: n_ff                  = 3584
+0.00.608.211 I print_info: n_expert              = 0
+0.00.608.211 I print_info: n_expert_used         = 0
+0.00.608.212 I print_info: n_expert_groups       = 0
+0.00.608.212 I print_info: n_group_used          = 0
+0.00.608.212 I print_info: causal attn           = 1
+0.00.608.212 I print_info: pooling type          = -1
+0.00.608.212 I print_info: rope type             = 40
+0.00.608.213 I print_info: rope scaling          = linear
+0.00.608.214 I print_info: freq_base_train       = 10000000.0
+0.00.608.214 I print_info: freq_scale_train      = 1
+0.00.608.214 I print_info: n_ctx_orig_yarn       = 262144
+0.00.608.215 I print_info: rope_yarn_log_mul     = 0.0000
+0.00.608.215 I print_info: rope_finetuned        = unknown
+0.00.608.215 I print_info: mrope sections        = [11, 11, 10, 0]
+0.00.608.216 I print_info: ssm_d_conv            = 4
+0.00.608.216 I print_info: ssm_d_inner           = 2048
+0.00.608.216 I print_info: ssm_d_state           = 128
+0.00.608.216 I print_info: ssm_dt_rank           = 16
+0.00.608.216 I print_info: ssm_n_group           = 16
+0.00.608.217 I print_info: ssm_dt_b_c_rms        = 0
+0.00.608.217 I print_info: model type            = ?B
+0.00.608.218 I print_info: model params          = 337.30 M
+0.00.608.219 I print_info: general.name          = Qwen35 0.8b Text N4
+0.00.608.219 I print_info: vocab type            = BPE
+0.00.608.220 I print_info: n_vocab               = 248320
+0.00.608.220 I print_info: n_merges              = 247587
+0.00.608.220 I print_info: BOS token             = 11 ','
+0.00.608.221 I print_info: EOS token             = 248044 '<|endoftext|>'
+0.00.608.221 I print_info: EOT token             = 248046 '<|im_end|>'
+0.00.608.221 I print_info: PAD token             = 248044 '<|endoftext|>'
+0.00.608.221 I print_info: LF token              = 198 'Ċ'
+0.00.608.222 I print_info: FIM PRE token         = 248060 '<|fim_prefix|>'
+0.00.608.222 I print_info: FIM SUF token         = 248062 '<|fim_suffix|>'
+0.00.608.222 I print_info: FIM MID token         = 248061 '<|fim_middle|>'
+0.00.608.222 I print_info: FIM PAD token         = 248063 '<|fim_pad|>'
+0.00.608.223 I print_info: FIM REP token         = 248064 '<|repo_name|>'
+0.00.608.223 I print_info: FIM SEP token         = 248065 '<|file_sep|>'
+0.00.608.223 I print_info: EOG token             = 248044 '<|endoftext|>'
+0.00.608.224 I print_info: EOG token             = 248046 '<|im_end|>'
+0.00.608.224 I print_info: EOG token             = 248063 '<|fim_pad|>'
+0.00.608.224 I print_info: EOG token             = 248064 '<|repo_name|>'
+0.00.608.225 I print_info: EOG token             = 248065 '<|file_sep|>'
+0.00.608.225 I print_info: max token length      = 256
+0.00.608.226 I load_tensors: loading model tensors, this can take a while... (mmap = true, direct_io = false)
+0.00.609.820 D load_tensors: layer   0 assigned to device Vulkan0, is_swa = 0
+0.00.609.822 D load_tensors: layer   1 assigned to device Vulkan0, is_swa = 0
+0.00.609.823 D load_tensors: layer   2 assigned to device Vulkan0, is_swa = 0
+0.00.609.823 D load_tensors: layer   3 assigned to device Vulkan0, is_swa = 0
+0.00.609.823 D load_tensors: layer   4 assigned to device Vulkan0, is_swa = 0
+0.00.609.852 D create_tensor: loading tensor token_embd.weight
+0.00.609.860 D create_tensor: loading tensor output_norm.weight
+0.00.609.866 D create_tensor: loading tensor token_embd.weight
+0.00.609.870 D create_tensor: loading tensor blk.0.attn_norm.weight
+0.00.609.876 D create_tensor: loading tensor blk.0.post_attention_norm.weight
+0.00.609.881 D create_tensor: loading tensor blk.0.attn_qkv.weight
+0.00.609.885 D create_tensor: loading tensor blk.0.attn_gate.weight
+0.00.609.891 D create_tensor: loading tensor blk.0.ssm_conv1d.weight
+0.00.609.895 D create_tensor: loading tensor blk.0.ssm_dt.bias
+0.00.609.899 D create_tensor: loading tensor blk.0.ssm_a
+0.00.609.903 D create_tensor: loading tensor blk.0.ssm_beta.weight
+0.00.609.907 D create_tensor: loading tensor blk.0.ssm_alpha.weight
+0.00.609.911 D create_tensor: loading tensor blk.0.ssm_norm.weight
+0.00.609.915 D create_tensor: loading tensor blk.0.ssm_out.weight
+0.00.609.920 D create_tensor: loading tensor blk.0.ffn_gate.weight
+0.00.609.924 D create_tensor: loading tensor blk.0.ffn_down.weight
+0.00.609.928 D create_tensor: loading tensor blk.0.ffn_up.weight
+0.00.609.932 D create_tensor: loading tensor blk.1.attn_norm.weight
+0.00.609.937 D create_tensor: loading tensor blk.1.post_attention_norm.weight
+0.00.609.941 D create_tensor: loading tensor blk.1.attn_qkv.weight
+0.00.609.946 D create_tensor: loading tensor blk.1.attn_gate.weight
+0.00.609.951 D create_tensor: loading tensor blk.1.ssm_conv1d.weight
+0.00.609.958 D create_tensor: loading tensor blk.1.ssm_dt.bias
+0.00.609.963 D create_tensor: loading tensor blk.1.ssm_a
+0.00.609.967 D create_tensor: loading tensor blk.1.ssm_beta.weight
+0.00.609.971 D create_tensor: loading tensor blk.1.ssm_alpha.weight
+0.00.609.976 D create_tensor: loading tensor blk.1.ssm_norm.weight
+0.00.609.983 D create_tensor: loading tensor blk.1.ssm_out.weight
+0.00.609.987 D create_tensor: loading tensor blk.1.ffn_gate.weight
+0.00.609.992 D create_tensor: loading tensor blk.1.ffn_down.weight
+0.00.609.996 D create_tensor: loading tensor blk.1.ffn_up.weight
+0.00.610.002 D create_tensor: loading tensor blk.2.attn_norm.weight
+0.00.610.007 D create_tensor: loading tensor blk.2.post_attention_norm.weight
+0.00.610.012 D create_tensor: loading tensor blk.2.attn_qkv.weight
+0.00.610.019 D create_tensor: loading tensor blk.2.attn_gate.weight
+0.00.610.023 D create_tensor: loading tensor blk.2.ssm_conv1d.weight
+0.00.610.028 D create_tensor: loading tensor blk.2.ssm_dt.bias
+0.00.610.032 D create_tensor: loading tensor blk.2.ssm_a
+0.00.610.039 D create_tensor: loading tensor blk.2.ssm_beta.weight
+0.00.610.043 D create_tensor: loading tensor blk.2.ssm_alpha.weight
+0.00.610.048 D create_tensor: loading tensor blk.2.ssm_norm.weight
+0.00.610.052 D create_tensor: loading tensor blk.2.ssm_out.weight
+0.00.610.059 D create_tensor: loading tensor blk.2.ffn_gate.weight
+0.00.610.063 D create_tensor: loading tensor blk.2.ffn_down.weight
+0.00.610.068 D create_tensor: loading tensor blk.2.ffn_up.weight
+0.00.610.072 D create_tensor: loading tensor blk.3.attn_norm.weight
+0.00.610.079 D create_tensor: loading tensor blk.3.post_attention_norm.weight
+0.00.610.086 D create_tensor: loading tensor blk.3.attn_q.weight
+0.00.610.092 D create_tensor: loading tensor blk.3.attn_k.weight
+0.00.610.097 D create_tensor: loading tensor blk.3.attn_v.weight
+0.00.610.107 D create_tensor: loading tensor blk.3.attn_output.weight
+0.00.610.115 D create_tensor: loading tensor blk.3.attn_q_norm.weight
+0.00.610.120 D create_tensor: loading tensor blk.3.attn_k_norm.weight
+0.00.610.125 D create_tensor: loading tensor blk.3.ffn_gate.weight
+0.00.610.131 D create_tensor: loading tensor blk.3.ffn_down.weight
+0.00.610.135 D create_tensor: loading tensor blk.3.ffn_up.weight
+0.00.610.233 D done_getting_tensors: tensor 'token_embd.weight' (bf16) (and 0 others) cannot be used with preferred buffer type Vulkan_Host, using CPU instead
+0.00.667.027 I load_tensors: offloading output layer to GPU
+0.00.667.031 I load_tensors: offloading 3 repeating layers to GPU
+0.00.667.032 I load_tensors: offloaded 5/5 layers to GPU
+0.00.667.038 I load_tensors:   CPU_Mapped model buffer size =   485.00 MiB
+0.00.667.039 I load_tensors:      Vulkan0 model buffer size =   643.51 MiB
+0.00.871.933 I cmn  common_init_: added <|endoftext|> logit bias = -inf
+0.00.871.937 I cmn  common_init_: added <|im_end|> logit bias = -inf
+0.00.871.937 I cmn  common_init_: added <|fim_pad|> logit bias = -inf
+0.00.871.938 I cmn  common_init_: added <|repo_name|> logit bias = -inf
+0.00.871.939 I cmn  common_init_: added <|file_sep|> logit bias = -inf
+0.00.871.988 I llama_context: constructing llama_context
+0.00.871.992 I llama_context: n_seq_max     = 1
+0.00.871.992 I llama_context: n_ctx         = 256
+0.00.871.992 I llama_context: n_ctx_seq     = 256
+0.00.871.993 I llama_context: n_batch       = 128
+0.00.871.993 I llama_context: n_ubatch      = 1
+0.00.871.993 I llama_context: causal_attn   = 1
+0.00.871.994 I llama_context: flash_attn    = auto
+0.00.871.994 I llama_context: kv_unified    = false
+0.00.871.999 I llama_context: freq_base     = 10000000.0
+0.00.872.000 I llama_context: freq_scale    = 1
+0.00.872.000 I llama_context: n_rs_seq      = 0
+0.00.872.001 I llama_context: n_outputs_max = 1
+0.00.872.001 I llama_context: n_ctx_seq (256) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
+0.00.872.020 D set_abort_callback: call
+0.00.872.277 I llama_context: Vulkan_Host  output buffer size =     0.95 MiB
+0.00.872.292 D llama_kv_cache: layer   0: filtered
+0.00.872.293 D llama_kv_cache: layer   1: filtered
+0.00.872.293 D llama_kv_cache: layer   2: filtered
+0.00.872.294 D llama_kv_cache: layer   3: dev = Vulkan0
+0.00.872.342 I llama_kv_cache:    Vulkan0 KV buffer size =     0.50 MiB
+0.00.872.849 I llama_kv_cache: size =    0.50 MiB (   256 cells,   1 layers,  1/1 seqs), K (f16):    0.25 MiB, V (f16):    0.25 MiB
+0.00.872.851 I llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
+0.00.872.852 I llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
+0.00.872.853 D llama_memory_recurrent, layer   0: dev = Vulkan0
+0.00.872.855 D llama_memory_recurrent, layer   1: dev = Vulkan0
+0.00.872.856 D llama_memory_recurrent, layer   2: dev = Vulkan0
+0.00.872.856 D llama_memory_recurrent: layer   3: skipped
+0.00.873.016 I llama_memory_recurrent:    Vulkan0 RS buffer size =     3.21 MiB
+0.00.873.020 I llama_memory_recurrent: size =    3.21 MiB (     1 cells,   4 layers,  1 seqs  0 rs_seq), R (f32):    0.21 MiB, S (f32):    3.00 MiB
+0.00.873.021 D llama_context: enumerating backends
+0.00.873.022 D llama_context: backend_ptrs.size() = 2
+0.00.873.023 I sched_reserve: reserving ...
+0.00.873.024 D sched_reserve: max_nodes = 1792
+0.00.873.268 D sched_reserve: reserving full memory module
+0.00.873.272 D sched_reserve: worst-case: n_tokens = 1, n_seqs = 1, n_outputs = 1
+0.00.873.273 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.873.543 I resolve_fused_ops: Flash Attention enabled
+0.00.873.545 I resolve_fused_ops: resolving fused Gated Delta Net support:
+0.00.873.546 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.873.751 I resolve_fused_ops: fused Gated Delta Net (autoregressive) enabled
+0.00.873.753 D graph_reserve: reserving a graph for ubatch with n_tokens =   16, n_seqs =  1, n_outputs =   16
+0.00.873.948 I resolve_fused_ops: fused Gated Delta Net (chunked) enabled
+0.00.873.950 I resolve_fused_ops: resolving fused Lightning Indexer support:
+0.00.873.951 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.874.135 I resolve_fused_ops: Lightning Indexer enabled
+0.00.874.137 I resolve_fused_ops: resolving fused DeepSeek V4 HC support:
+0.00.874.137 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.874.318 I resolve_fused_ops: fused DeepSeek V4 HC pre enabled
+0.00.874.320 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.874.499 I resolve_fused_ops: fused DeepSeek V4 HC comb enabled
+0.00.874.501 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.874.678 I resolve_fused_ops: fused DeepSeek V4 HC post enabled
+0.00.874.680 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.874.989 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.875.230 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.875.464 I sched_reserve:    Vulkan0 compute buffer size =     2.12 MiB
+0.00.875.466 I sched_reserve: Vulkan_Host compute buffer size =     0.04 MiB
+0.00.875.466 I sched_reserve: graph nodes  = 235
+0.00.875.467 I sched_reserve: graph splits = 2
+0.00.875.467 I sched_reserve: reserve took 2.44 ms, sched copies = 1
+0.00.875.570 D set_adapters_lora: adapters = (nil)
+0.00.875.572 D adapters_lora_are_same: adapters = (nil)
+0.00.875.572 I cmn  common_init_: warming up the model with an empty run - please wait ... (--no-warmup to disable)
+0.00.898.304 I cmn  common_conte: the context does not support partial sequence removal
+0.00.908.373 I srv    load_model: speculative decoding will use checkpoints
+0.00.908.376 I srv    load_model: initializing, n_slots = 1, n_ctx_slot = 256, kv_unified = 'false'
+0.00.908.378 I spec common_specu: no implementations specified for speculative decoding
+0.00.908.379 I slot   load_model: id  0 | task -1 | new slot, n_ctx = 256
+0.00.908.379 D slot        reset: id  0 | task -1 | 
+0.00.908.386 I srv    load_model: prompt cache is enabled, size limit: 8192 MiB
+0.00.908.386 I srv    load_model: use `--cache-ram 0` to disable the prompt cache
+0.00.908.386 I srv    load_model: for more info see https://github.com/ggml-org/llama.cpp/pull/16391
+0.00.908.387 I srv    load_model: context checkpoints enabled, max = 32, min spacing = 8192
+0.00.908.399 I srv          init: idle slots will be saved to prompt cache upon starting a new task
+0.00.908.399 D srv          init: __TEST_TAG_CACHE_IDLE_SLOTS_ENABLED__
+0.00.911.479 D common_chat_templates_apply_jinja: using differential autoparser
+0.00.911.483 D === Starting differential analysis ===
+0.00.911.483 D Phase 1: Reasoning analysis
+0.00.913.365 D Phase 2: Content analysis
+0.00.915.044 D Phase 3: Tool call analysis
+0.00.917.207 D Phase 3a: Function call analysis
+0.00.918.268 D Phase 3b: Argument analysis
+0.00.922.293 D Phase 3c: Call id analysis
+0.00.923.908 D Template application failed: 
+------------
+While executing CallExpression at line 43, column 24 in source:
+... not messages %}↵    {{- raise_exception('No messages provided.') }}↵{%- endif %...
+                                           ^
+Error: Jinja Exception: No messages provided.
+0.00.924.082 D detect_user_start_marker: Template application failed, unsupported empty messages? trying complex variant
+0.00.924.694 D 
+--- Reasoning & Content Structure ---
+0.00.924.696 D user_msg_start: <|im_start|>user
+0.00.924.696 D assistant_msg_start: <|im_start|>assistant
+0.00.924.697 D reasoning_mode: TAG_BASED
+0.00.924.697 D reasoning_start: '<think>
+'
+0.00.924.697 D reasoning_end: '
+</think>
+
+'
+0.00.924.698 D content_mode: PLAIN
+0.00.924.698 D content_start: ''
+0.00.924.698 D content_end: ''
+0.00.924.699 D 
+--- Tool Call Structure ---
+0.00.924.699 D tool_mode: TAG_WITH_TAGGED
+0.00.924.699 D supports_tools: true
+0.00.924.699 D supports_parallel_calls: true
+0.00.924.700 D tool_section_start: ''
+0.00.924.700 D tool_section_end: ''
+0.00.924.700 D per_call_start: '<tool_call>
+'
+0.00.924.700 D per_call_end: '</tool_call>'
+0.00.924.700 D func_name_prefix: '<function='
+0.00.924.700 D func_name_suffix: '>
+'
+0.00.924.701 D func_args_separator: ''
+0.00.924.701 D func_close: '</function>
+'
+0.00.924.701 D call_id_prefix: ''
+0.00.924.702 D call_id_suffix: ''
+0.00.924.703 D call_id_pos: 'NONE'
+0.00.924.703 D args_start: ''
+0.00.924.703 D args_end: ''
+0.00.924.703 D arg_name_prefix: '<parameter='
+0.00.924.703 D arg_name_suffix: '>
+'
+0.00.924.703 D arg_value_prefix: ''
+0.00.924.703 D arg_value_suffix: '
+</parameter>
+'
+0.00.924.704 D name_field: 'name'
+0.00.924.704 D args_field: 'arguments'
+0.00.924.704 D id_field: ''
+0.00.924.704 D gen_id_field: ''
+0.00.924.704 D parameter_order: ''
+0.00.924.704 D === Differential analysis complete ===
+0.00.926.069 D common_chat_templates_apply_jinja: generated parser:
+Sequence(Literal(<|im_start|>assistant
+), Space, Epsilon, Tag(content, Until()), End)
+
+parser generation prompt: <|im_start|>assistant
+<think>
+
+0.00.926.077 I srv          init: init: chat template, example_format: '<|im_start|>system
+You are a helpful assistant<|im_end|>
+<|im_start|>user
+Hello<|im_end|>
+<|im_start|>assistant
+Hi there<|im_end|>
+<|im_start|>user
+How are you?<|im_end|>
+<|im_start|>assistant
+<think>
+'
+0.00.926.123 D common_chat_templates_apply_jinja: using differential autoparser
+0.00.926.126 D === Starting differential analysis ===
+0.00.926.126 D Phase 1: Reasoning analysis
+0.00.927.980 D Phase 2: Content analysis
+0.00.929.622 D Phase 3: Tool call analysis
+0.00.931.765 D Phase 3a: Function call analysis
+0.00.932.842 D Phase 3b: Argument analysis
+0.00.936.793 D Phase 3c: Call id analysis
+0.00.938.298 D Template application failed: 
+------------
+While executing CallExpression at line 43, column 24 in source:
+... not messages %}↵    {{- raise_exception('No messages provided.') }}↵{%- endif %...
+                                           ^
+Error: Jinja Exception: No messages provided.
+0.00.938.473 D detect_user_start_marker: Template application failed, unsupported empty messages? trying complex variant
+0.00.939.098 D 
+--- Reasoning & Content Structure ---
+0.00.939.102 D user_msg_start: <|im_start|>user
+0.00.939.102 D assistant_msg_start: <|im_start|>assistant
+0.00.939.103 D reasoning_mode: TAG_BASED
+0.00.939.103 D reasoning_start: '<think>
+'
+0.00.939.104 D reasoning_end: '
+</think>
+
+'
+0.00.939.104 D content_mode: PLAIN
+0.00.939.105 D content_start: ''
+0.00.939.105 D content_end: ''
+0.00.939.105 D 
+--- Tool Call Structure ---
+0.00.939.105 D tool_mode: TAG_WITH_TAGGED
+0.00.939.106 D supports_tools: true
+0.00.939.106 D supports_parallel_calls: true
+0.00.939.106 D tool_section_start: ''
+0.00.939.106 D tool_section_end: ''
+0.00.939.106 D per_call_start: '<tool_call>
+'
+0.00.939.107 D per_call_end: '</tool_call>'
+0.00.939.107 D func_name_prefix: '<function='
+0.00.939.107 D func_name_suffix: '>
+'
+0.00.939.107 D func_args_separator: ''
+0.00.939.107 D func_close: '</function>
+'
+0.00.939.107 D call_id_prefix: ''
+0.00.939.108 D call_id_suffix: ''
+0.00.939.108 D call_id_pos: 'NONE'
+0.00.939.108 D args_start: ''
+0.00.939.109 D args_end: ''
+0.00.939.109 D arg_name_prefix: '<parameter='
+0.00.939.109 D arg_name_suffix: '>
+'
+0.00.939.109 D arg_value_prefix: ''
+0.00.939.109 D arg_value_suffix: '
+</parameter>
+'
+0.00.939.109 D name_field: 'name'
+0.00.939.110 D args_field: 'arguments'
+0.00.939.110 D id_field: ''
+0.00.939.110 D gen_id_field: ''
+0.00.939.110 D parameter_order: ''
+0.00.939.110 D === Differential analysis complete ===
+0.00.939.851 D common_chat_templates_apply_jinja: generated parser:
+Sequence(Literal(<|im_start|>assistant
+), Space, Repetition(Sequence(Epsilon, Literal(<think>), Repetition(Literal(
+), 0, 1), Tag(reasoning, Until(</think>)), Epsilon, Repetition(Literal(
+), 0, 1), Literal(</think>), Repetition(Literal(
+), 0, 1), Repetition(Literal(
+), 0, 1)), 0, 1), Tag(content, Until()), End)
+
+parser generation prompt: <|im_start|>assistant
+<think>
+
+0.00.939.858 I srv          init: init: chat template, thinking = 1
+0.00.939.876 I srv  llama_server: model loaded
+0.00.939.879 I srv  llama_server: listening on http://127.0.0.1:60907
+0.00.939.881 D que    start_loop: processing new tasks
+0.00.939.882 D que    start_loop: update slots
+0.00.939.884 I srv  update_slots: all slots are idle
+0.00.939.884 D que    start_loop: waiting for new tasks
+0.00.972.405 D srv          stop: all tasks already finished, no need to cancel
+0.00.972.745 D srv          stop: all tasks already finished, no need to cancel
+0.00.973.112 D srv          stop: all tasks already finished, no need to cancel
+0.00.973.491 D common_chat_templates_apply_jinja: using differential autoparser
+0.00.973.493 D === Starting differential analysis ===
+0.00.973.493 D Phase 1: Reasoning analysis
+0.00.975.467 D Phase 2: Content analysis
+0.00.977.158 D Phase 3: Tool call analysis
+0.00.979.417 D Phase 3a: Function call analysis
+0.00.980.509 D Phase 3b: Argument analysis
+0.00.984.655 D Phase 3c: Call id analysis
+0.00.986.770 D Template application failed: 
+------------
+While executing CallExpression at line 43, column 24 in source:
+... not messages %}↵    {{- raise_exception('No messages provided.') }}↵{%- endif %...
+                                           ^
+Error: Jinja Exception: No messages provided.
+0.00.987.011 D detect_user_start_marker: Template application failed, unsupported empty messages? trying complex variant
+0.00.987.656 D 
+--- Reasoning & Content Structure ---
+0.00.987.659 D user_msg_start: <|im_start|>user
+0.00.987.659 D assistant_msg_start: <|im_start|>assistant
+0.00.987.660 D reasoning_mode: TAG_BASED
+0.00.987.660 D reasoning_start: '<think>
+'
+0.00.987.660 D reasoning_end: '
+</think>
+
+'
+0.00.987.660 D content_mode: PLAIN
+0.00.987.661 D content_start: ''
+0.00.987.661 D content_end: ''
+0.00.987.661 D 
+--- Tool Call Structure ---
+0.00.987.661 D tool_mode: TAG_WITH_TAGGED
+0.00.987.661 D supports_tools: true
+0.00.987.662 D supports_parallel_calls: true
+0.00.987.662 D tool_section_start: ''
+0.00.987.662 D tool_section_end: ''
+0.00.987.662 D per_call_start: '<tool_call>
+'
+0.00.987.662 D per_call_end: '</tool_call>'
+0.00.987.662 D func_name_prefix: '<function='
+0.00.987.663 D func_name_suffix: '>
+'
+0.00.987.663 D func_args_separator: ''
+0.00.987.663 D func_close: '</function>
+'
+0.00.987.663 D call_id_prefix: ''
+0.00.987.663 D call_id_suffix: ''
+0.00.987.664 D call_id_pos: 'NONE'
+0.00.987.664 D args_start: ''
+0.00.987.664 D args_end: ''
+0.00.987.664 D arg_name_prefix: '<parameter='
+0.00.987.664 D arg_name_suffix: '>
+'
+0.00.987.664 D arg_value_prefix: ''
+0.00.987.664 D arg_value_suffix: '
+</parameter>
+'
+0.00.987.665 D name_field: 'name'
+0.00.987.665 D args_field: 'arguments'
+0.00.987.666 D id_field: ''
+0.00.987.666 D gen_id_field: ''
+0.00.987.666 D parameter_order: ''
+0.00.987.666 D === Differential analysis complete ===
+0.00.988.530 D common_chat_templates_apply_jinja: generated parser:
+Sequence(Literal(<|im_start|>assistant
+), Space, Repetition(Sequence(Epsilon, Literal(<think>), Repetition(Literal(
+), 0, 1), Tag(reasoning, Until(</think>)), Epsilon, Repetition(Literal(
+), 0, 1), Literal(</think>), Repetition(Literal(
+), 0, 1), Repetition(Literal(
+), 0, 1)), 0, 1), Tag(content, Until()), End)
+
+parser generation prompt: <|im_start|>assistant
+<think>
+
+0.00.988.564 I srv  server_strea: conv_id= (empty=1)
+0.00.988.697 I srv    operator(): chat format: peg-native
+0.00.988.824 D srv  eval_llama_c: reasoning budget: tokens=-1, generation_prompt='<|im_start|>assistant
+<think>
+', start=1 toks, end=1 toks, forced=1 toks
+0.00.988.847 D res  add_waiting_: add task 0 to waiting list. current waiting = 0 (before add)
+0.00.988.850 D que          post: new task, id = 0/1, front = 0
+0.00.988.869 D que    start_loop: processing new tasks
+0.00.988.876 D que    start_loop: processing task, id = 0
+0.00.988.878 I slot get_availabl: id  0 | task -1 | selected slot by LRU, t_last = -1
+0.00.988.879 I srv  get_availabl: updating prompt cache
+0.00.988.883 I srv          load:  - looking for better prompt, base f_keep = -1.000, sim = 0.000
+0.00.988.886 I srv        update:  - cache state: 0 prompts, 0.000 MiB (limits: 8192.000 MiB, 256 tokens, 8589934592 est)
+0.00.988.889 I srv  get_availabl: prompt cache update took 0.01 ms
+0.00.988.903 D slot launch_slot_: id  0 | task -1 | launching slot : {"id":0,"n_ctx":256,"speculative":false,"is_processing":false}
+0.00.988.940 D common_sampler_init: prefill token: 248045 = <|im_start|>
+0.00.988.942 D common_sampler_init: prefill token: 74455 = assistant
+0.00.988.943 D common_sampler_init: prefill token: 198 = 
+
+0.00.988.943 D common_sampler_init: prefill token: 248068 = <think>
+0.00.988.943 D common_sampler_init: prefill token: 198 = 
+
+0.00.988.955 I slot launch_slot_: id  0 | task -1 | sampler chain: logits -> ?penalties -> ?dry -> ?top-n-sigma -> top-k -> ?typical -> top-p -> min-p -> ?xtc -> temp-ext -> dist 
+0.00.988.960 I slot launch_slot_: id  0 | task -1 | sampler params: 
+	repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
+	dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 256
+	top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.800
+	mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000, adaptive_target = -1.000, adaptive_decay = 0.900
+0.00.988.962 I slot launch_slot_: id  0 | task 0 | processing task, is_child = 0
+0.00.988.963 D que    start_loop: update slots
+0.00.988.963 D srv  update_slots: posting NEXT_RESPONSE
+0.00.988.964 D que          post: new task, id = 1, front = 0
+0.00.988.968 I slot   operator(): id  0 | task 0 | new prompt, n_ctx_slot = 256, n_keep = 0, task.n_tokens = 23
+0.00.988.971 D res          send: sending result for task id = 0
+0.00.988.971 D res          send: task id = 0 pushed to result queue
+0.00.988.975 I slot   operator(): id  0 | task 0 | cached n_tokens = 0, memory_seq_rm [0, end)
+0.00.988.980 D slot   operator(): id  0 | task 0 | main/do_checkpoint = no, pos_min = -1, pos_max = -1
+0.00.988.983 D set_adapters_lora: adapters = (nil)
+0.00.988.983 D adapters_lora_are_same: adapters = (nil)
+0.00.988.984 D set_embeddings: value = 0
+0.00.988.985 D srv        decode: n_batch (effective) = 128, off = 0
+0.00.988.994 D srv          stop: all tasks already finished, no need to cancel
+0.01.062.912 D que    start_loop: waiting for new tasks
+0.01.062.915 D que    start_loop: processing new tasks
+0.01.062.919 D que    start_loop: processing task, id = 1
+0.01.062.921 D que    start_loop: update slots
+0.01.062.921 D srv  update_slots: posting NEXT_RESPONSE
+0.01.062.923 D que          post: new task, id = 2, front = 0
+0.01.062.928 I slot   operator(): id  0 | task 0 | cached n_tokens = 18, memory_seq_rm [18, end)
+0.01.062.932 D slot   operator(): id  0 | task 0 | main/do_checkpoint = no, pos_min = 17, pos_max = 17
+0.01.062.935 D set_adapters_lora: adapters = (nil)
+0.01.062.936 D adapters_lora_are_same: adapters = (nil)
+0.01.062.936 D set_embeddings: value = 0
+0.01.062.937 D srv        decode: n_batch (effective) = 128, off = 0
+0.01.066.368 D que    start_loop: waiting for new tasks
+0.01.066.370 D que    start_loop: processing new tasks
+0.01.066.371 D que    start_loop: processing task, id = 2
+0.01.066.371 D que    start_loop: update slots
+0.01.066.372 D srv  update_slots: posting NEXT_RESPONSE
+0.01.066.372 D que          post: new task, id = 3, front = 0
+0.01.066.374 I slot   operator(): id  0 | task 0 | cached n_tokens = 19, memory_seq_rm [19, end)
+0.01.066.386 I slot init_sampler: id  0 | task 0 | init sampler, took 0.00 ms, tokens: text = 23, total = 23
+0.01.066.387 D slot   operator(): id  0 | task 0 | main/do_checkpoint = yes, pos_min = 18, pos_max = 18
+0.01.070.179 I slot create_check: id  0 | task 0 | created context checkpoint 1 of 32 (pos_min = 18, pos_max = 18, n_tokens = 19, size = 3.211 MiB)
+0.01.070.183 D set_adapters_lora: adapters = (nil)
+0.01.070.183 D adapters_lora_are_same: adapters = (nil)
+0.01.070.184 D set_embeddings: value = 0
+0.01.070.185 D srv        decode: n_batch (effective) = 128, off = 0
+0.01.089.802 D res          send: sending result for task id = 0
+0.01.089.806 D res          send: task id = 0 pushed to result queue
+0.01.089.810 D slot process_toke: id  0 | task 0 | n_decoded = 1, n_remaining = 3, next token: 134276 '品牌和'
+0.01.089.813 D que    start_loop: waiting for new tasks
+0.01.089.814 D que    start_loop: processing new tasks
+0.01.089.817 D que    start_loop: processing task, id = 3
+0.01.089.819 D que    start_loop: update slots
+0.01.089.820 D srv  update_slots: posting NEXT_RESPONSE
+0.01.089.821 D que          post: new task, id = 4, front = 0
+0.01.089.828 D slot handle_last_: id  0 | task 0 | slot decode token, id=134276, n_ctx = 256, n_tokens = 23, truncated = 0
+0.01.089.832 D set_adapters_lora: adapters = (nil)
+0.01.089.833 D adapters_lora_are_same: adapters = (nil)
+0.01.089.834 D set_embeddings: value = 0
+0.01.089.834 D srv        decode: n_batch (effective) = 128, off = 0
+0.01.090.015 D srv    operator(): http: streamed chunk: data: {"choices":[{"finish_reason":null,"index":0,"delta":{"role":"assistant","content":null}}],"created":1786987488,"id":"chatcmpl-1CD2oLYnRNra1L8lUQXSX8vyJjG0oQFd","model":"MVP/artifacts/qwen35-text-n4-bf16.gguf","system_fingerprint":"b10068-571d0d540d","object":"chat.completion.chunk"}
+
+data: {"choices":[{"finish_reason":null,"index":0,"delta":{"reasoning_content":"品牌和"}}],"created":1786987488,"id":"chatcmpl-1CD2oLYnRNra1L8lUQXSX8vyJjG0oQFd","model":"MVP/artifacts/qwen35-text-n4-bf16.gguf","system_fingerprint":"b10068-571d0d540d","object":"chat.completion.chunk","timings":{"cache_n":0,"prompt_n":23,"prompt_ms":100.822,"prompt_per_token_ms":4.383565217391305,"prompt_per_second":228.1248140286842,"predicted_n":1,"predicted_ms":0.001,"predicted_per_token_ms":0.001,"predicted_per_second":1000000.0}}
+
+
+
+
+Loading model... 
+
+▄▄ ▄▄
+██ ██
+██ ██  ▀▀█▄ ███▄███▄  ▀▀█▄    ▄████ ████▄ ████▄
+██ ██ ▄█▀██ ██ ██ ██ ▄█▀██    ██    ██ ██ ██ ██
+██ ██ ▀█▄██ ██ ██ ██ ▀█▄██ ██ ▀████ ████▀ ████▀
+                                    ██    ██
+                                    ▀▀    ▀▀
+
+build      : b10068-571d0d540d
+model      : MVP/artifacts/qwen35-text-n4-bf16.gguf
+ftype      : BF16
+modalities : text
+
+available commands:
+  /exit or Ctrl+C     stop or exit
+  /regen              regenerate the last response
+  /clear              clear the chat history
+  /read <file>        add a text file
+  /glob <pattern>     add text files using globbing pattern
+
+
+
+> Kısa cevap ver: 2+2 kaçtır?
+
+[Start thinking]
+
+品牌和0.01.101.192 D res          send: sending result for task id = 0
+0.01.101.197 D res          send: task id = 0 pushed to result queue
+0.01.101.199 D slot process_toke: id  0 | task 0 | n_decoded = 2, n_remaining = 2, next token: 59253 'ój'
+0.01.101.200 D que    start_loop: waiting for new tasks
+0.01.101.200 D que    start_loop: processing new tasks
+0.01.101.201 D que    start_loop: processing task, id = 4
+0.01.101.202 D que    start_loop: update slots
+0.01.101.202 D srv  update_slots: posting NEXT_RESPONSE
+0.01.101.203 D que          post: new task, id = 5, front = 0
+0.01.101.205 D slot handle_last_: id  0 | task 0 | slot decode token, id=59253, n_ctx = 256, n_tokens = 24, truncated = 0
+0.01.101.206 D set_adapters_lora: adapters = (nil)
+0.01.101.206 D adapters_lora_are_same: adapters = (nil)
+0.01.101.219 D set_embeddings: value = 0
+0.01.101.220 D srv        decode: n_batch (effective) = 128, off = 0
+0.01.101.353 D srv    operator(): http: streamed chunk: data: {"choices":[{"finish_reason":null,"index":0,"delta":{"reasoning_content":"ój"}}],"created":1786987488,"id":"chatcmpl-1CD2oLYnRNra1L8lUQXSX8vyJjG0oQFd","model":"MVP/artifacts/qwen35-text-n4-bf16.gguf","system_fingerprint":"b10068-571d0d540d","object":"chat.completion.chunk","timings":{"cache_n":0,"prompt_n":23,"prompt_ms":100.822,"prompt_per_token_ms":4.383565217391305,"prompt_per_second":228.1248140286842,"predicted_n":2,"predicted_ms":11.399,"predicted_per_token_ms":5.6995,"predicted_per_second":175.45398719185894}}
+
+
+ój0.01.112.110 D res          send: sending result for task id = 0
+0.01.112.113 D res          send: task id = 0 pushed to result queue
+0.01.112.117 D slot process_toke: id  0 | task 0 | n_decoded = 3, n_remaining = 1, next token: 110092 '起的'
+0.01.112.120 D que    start_loop: waiting for new tasks
+0.01.112.121 D que    start_loop: processing new tasks
+0.01.112.124 D que    start_loop: processing task, id = 5
+0.01.112.126 D que    start_loop: update slots
+0.01.112.127 D srv  update_slots: posting NEXT_RESPONSE
+0.01.112.128 D que          post: new task, id = 6, front = 0
+0.01.112.136 D slot handle_last_: id  0 | task 0 | slot decode token, id=110092, n_ctx = 256, n_tokens = 25, truncated = 0
+0.01.112.141 D set_adapters_lora: adapters = (nil)
+0.01.112.141 D adapters_lora_are_same: adapters = (nil)
+0.01.112.142 D set_embeddings: value = 0
+0.01.112.143 D srv        decode: n_batch (effective) = 128, off = 0
+0.01.112.302 D srv    operator(): http: streamed chunk: data: {"choices":[{"finish_reason":null,"index":0,"delta":{"reasoning_content":"起的"}}],"created":1786987488,"id":"chatcmpl-1CD2oLYnRNra1L8lUQXSX8vyJjG0oQFd","model":"MVP/artifacts/qwen35-text-n4-bf16.gguf","system_fingerprint":"b10068-571d0d540d","object":"chat.completion.chunk","timings":{"cache_n":0,"prompt_n":23,"prompt_ms":100.822,"prompt_per_token_ms":4.383565217391305,"prompt_per_second":228.1248140286842,"predicted_n":3,"predicted_ms":22.31,"predicted_per_token_ms":7.4366666666666665,"predicted_per_second":134.46884805020173}}
+
+
+起的0.01.123.804 D res          send: sending result for task id = 0
+0.01.123.808 D res          send: task id = 0 pushed to result queue
+0.01.123.811 D slot process_toke: id  0 | task 0 | stopped by limit, n_decoded = 4, n_predict = 4
+0.01.123.812 D slot process_toke: id  0 | task 0 | n_decoded = 4, n_remaining = 0, next token: 53457 'rei'
+0.01.123.819 I slot print_timing: id  0 | task 0 | prompt eval time =     100.82 ms /    23 tokens (    4.38 ms per token,   228.12 tokens per second)
+0.01.123.820 I slot print_timing: id  0 | task 0 |        eval time =      34.01 ms /     4 tokens (    8.50 ms per token,   117.63 tokens per second)
+0.01.123.820 I slot print_timing: id  0 | task 0 |       total time =     134.83 ms /    27 tokens
+0.01.123.822 I slot print_timing: id  0 | task 0 |    graphs reused =         23
+0.01.123.836 D res          send: sending result for task id = 0
+0.01.123.836 D res          send: task id = 0 pushed to result queue
+0.01.123.837 I slot      release: id  0 | task 0 | stop processing: n_tokens = 26, truncated = 0
+0.01.123.838 D slot        reset: id  0 | task 0 | 
+0.01.123.843 D que    start_loop: waiting for new tasks
+0.01.123.844 D que    start_loop: processing new tasks
+0.01.123.846 D que    start_loop: processing task, id = 6
+0.01.123.847 D que    start_loop: update slots
+0.01.123.848 I srv  update_slots: all slots are idle
+0.01.123.848 D que    start_loop: waiting for new tasks
+0.01.123.977 D srv    operator(): http: streamed chunk: data: {"choices":[{"finish_reason":null,"index":0,"delta":{"reasoning_content":"rei"}}],"created":1786987488,"id":"chatcmpl-1CD2oLYnRNra1L8lUQXSX8vyJjG0oQFd","model":"MVP/artifacts/qwen35-text-n4-bf16.gguf","system_fingerprint":"b10068-571d0d540d","object":"chat.completion.chunk","timings":{"cache_n":0,"prompt_n":23,"prompt_ms":100.822,"prompt_per_token_ms":4.383565217391305,"prompt_per_second":228.1248140286842,"predicted_n":4,"predicted_ms":34.005,"predicted_per_token_ms":8.50125,"predicted_per_second":117.62976032936332}}
+
+
+0.01.124.004 D Parsed message: {"role":"assistant","content":"","reasoning_content":"品牌和ój起的rei"}
+rei0.01.124.869 D srv    operator(): http: streamed chunk: data: {"choices":[{"finish_reason":"length","index":0,"delta":{}}],"created":1786987488,"id":"chatcmpl-1CD2oLYnRNra1L8lUQXSX8vyJjG0oQFd","model":"MVP/artifacts/qwen35-text-n4-bf16.gguf","system_fingerprint":"b10068-571d0d540d","object":"chat.completion.chunk","timings":{"cache_n":0,"prompt_n":23,"prompt_ms":100.822,"prompt_per_token_ms":4.383565217391305,"prompt_per_second":228.1248140286842,"predicted_n":4,"predicted_ms":34.005,"predicted_per_token_ms":8.50125,"predicted_per_second":117.62976032936332},"__verbose":{"index":0,"content":"","tokens":[],"id_slot":0,"stop":true,"model":"MVP/artifacts/qwen35-text-n4-bf16.gguf","tokens_predicted":4,"tokens_evaluated":23,"generation_settings":{"seed":4294967295,"temperature":0.800000011920929,"dynatemp_range":0.0,"dynatemp_exponent":1.0,"top_k":40,"top_p":0.949999988079071,"min_p":0.05000000074505806,"top_n_sigma":-1.0,"xtc_probability":0.0,"xtc_threshold":0.10000000149011612,"typical_p":1.0,"repeat_last_n":64,"repeat_penalty":1.0,"presence_penalty":0.0,"frequency_penalty":0.0,"dry_multiplier":0.0,"dry_base":1.75,"dry_allowed_length":2,"dry_penalty_last_n":256,"dry_sequence_breakers":["\n",":","\"","*"],"mirostat":0,"mirostat_tau":5.0,"mirostat_eta":0.10000000149011612,"stop":[],"max_tokens":4,"n_predict":4,"n_keep":0,"n_discard":0,"ignore_eos":false,"stream":true,"logit_bias":[],"n_probs":0,"min_keep":0,"grammar":"","grammar_lazy":false,"grammar_triggers":[],"preserved_tokens":[29,248058,248059,248068,248069],"chat_format":"peg-native","reasoning_format":"deepseek","reasoning_in_content":false,"generation_prompt":"<|im_start|>assistant\n<think>\n","samplers":["penalties","dry","top_n_sigma","top_k","typ_p","top_p","min_p","xtc","temperature"],"speculative.types":"none","timings_per_token":true,"post_sampling_probs":false,"backend_sampling":false,"lora":[]},"prompt":"<|im_start|>user\nKısa cevap ver: 2+2 kaçtır?<|im_end|>\n<|im_start|>assistant\n<think>\n","has_new_line":false,"truncated":false,"stop_type":"limit","stopping_word":"","tokens_cached":26,"timings":{"cache_n":0,"prompt_n":23,"prompt_ms":100.822,"prompt_per_token_ms":4.383565217391305,"prompt_per_second":228.1248140286842,"predicted_n":4,"predicted_ms":34.005,"predicted_per_token_ms":8.50125,"predicted_per_second":117.62976032936332}}}
+
+
+0.01.124.875 D srv    operator(): all results received, terminating stream
+0.01.124.886 D srv    operator(): http: streamed chunk: data: [DONE]
+
+
+0.01.124.904 D srv    operator(): http: stream ended
+0.01.124.914 D res  remove_waiti: remove task 0 from waiting list. current waiting = 1 (before remove)
+0.01.124.915 D srv          stop: all tasks already finished, no need to cancel
+
+
+[ Prompt: 228.1 t/s | Generation: 117.6 t/s ]
+
+> 0.03.408.653 D que    start_loop: processing new tasks
+0.03.408.658 D que    start_loop: terminate
+0.03.408.660 I srv    operator(): operator(): cleaning up before exit...
+0.03.409.202 I common_memory_breakdown_print: | memory breakdown [MiB]                         | total   free    self   model   context   compute    unaccounted |
+0.03.409.205 I common_memory_breakdown_print: |   - Vulkan0 (RX 460 Graphics (RADV POLARIS11)) |  2048 =  802 + ( 649 =   643 +       3 +       2) +         596 |
+0.03.409.205 I common_memory_breakdown_print: |   - Host                                       |                  485 =   485 +       0 +       0                |
+0.03.409.302 D ~llama_context:    Vulkan0 compute buffer size is   2.1177 MiB, matches expectation of   2.1177 MiB
+0.03.409.307 D ~llama_context: Vulkan_Host compute buffer size is   0.0352 MiB, matches expectation of   0.0352 MiB
+
+
+Exiting...

MVP/evidence/student-vulkan-inference.log

@@ -0,0 +1,39 @@
+
+
+Loading model... /usr/src/debug/llama-cpp/llama.cpp/src/llama-context.cpp:1761: GGML_ASSERT(n_tokens_all <= cparams.n_batch) failed
+[New LWP 3840453]
+[New LWP 3840452]
+[New LWP 3840451]
+[New LWP 3840450]
+[New LWP 3840449]
+[New LWP 3840448]
+[New LWP 3840447]
+[New LWP 3840446]
+[New LWP 3840445]
+[New LWP 3840444]
+[New LWP 3840443]
+[New LWP 3840442]
+[New LWP 3840441]
+[New LWP 3840440]
+[New LWP 3840439]
+[New LWP 3840438]
+
+This GDB supports auto-downloading debuginfo from the following URLs:
+  <https://debuginfod.archlinux.org>
+  <https://debuginfod.cachyos.org>
+Enable debuginfod for this session? (y or [n]) [answered N; input not from terminal]
+Debuginfod has been disabled.
+To make this setting permanent, add 'set debuginfod enabled off' to .gdbinit.
+[Thread debugging using libthread_db enabled]
+Using host libthread_db library "/usr/lib/libthread_db.so.1".
+0x0000776dfb8b4b42 in ?? () from /usr/lib/libc.so.6
+#0  0x0000776dfb8b4b42 in ?? () from /usr/lib/libc.so.6
+#1  0x0000776dfb8a7de8 in ?? () from /usr/lib/libc.so.6
+#2  0x0000776dfb90162e in clock_nanosleep () from /usr/lib/libc.so.6
+#3  0x0000776dfb911f77 in nanosleep () from /usr/lib/libc.so.6
+#4  0x0000776dfbaf3bfb in cli_context::init() () from /usr/lib/libllama-cli-impl.so
+#5  0x0000776dfbaecb81 in llama_cli(int, char**) () from /usr/lib/libllama-cli-impl.so
+#6  0x0000776dfb827c8e in ?? () from /usr/lib/libc.so.6
+#7  0x0000776dfb827dcb in __libc_start_main () from /usr/lib/libc.so.6
+#8  0x000061b38279e055 in ?? ()
+[Inferior 1 (process 3840435) detached]

MVP/evidence/student-vulkan-no-host-forward.log

@@ -0,0 +1 @@
+{"index":0,"content":"日表示","tokens":[],"id_slot":0,"stop":true,"model":"MVP/artifacts/qwen35-text-n4-bf16.gguf","tokens_predicted":1,"tokens_evaluated":4,"generation_settings":{"seed":4294967295,"temperature":0.0,"dynatemp_range":0.0,"dynatemp_exponent":1.0,"top_k":40,"top_p":0.949999988079071,"min_p":0.05000000074505806,"top_n_sigma":-1.0,"xtc_probability":0.0,"xtc_threshold":0.10000000149011612,"typical_p":1.0,"repeat_last_n":64,"repeat_penalty":1.0,"presence_penalty":0.0,"frequency_penalty":0.0,"dry_multiplier":0.0,"dry_base":1.75,"dry_allowed_length":2,"dry_penalty_last_n":256,"dry_sequence_breakers":["\n",":","\"","*"],"mirostat":0,"mirostat_tau":5.0,"mirostat_eta":0.10000000149011612,"stop":[],"max_tokens":1,"n_predict":1,"n_keep":0,"n_discard":0,"ignore_eos":false,"stream":false,"logit_bias":[],"n_probs":0,"min_keep":0,"grammar":"","grammar_lazy":false,"grammar_triggers":[],"preserved_tokens":[],"chat_format":"Content-only","reasoning_format":"deepseek","reasoning_in_content":false,"generation_prompt":"","samplers":["penalties","dry","top_n_sigma","top_k","typ_p","top_p","min_p","xtc","temperature"],"speculative.types":"none","timings_per_token":false,"post_sampling_probs":false,"backend_sampling":false,"lora":[]},"prompt":"2+2=","has_new_line":false,"truncated":false,"stop_type":"limit","stopping_word":"","tokens_cached":4,"timings":{"cache_n":0,"prompt_n":4,"prompt_ms":30.446,"prompt_per_token_ms":7.6115,"prompt_per_second":131.38014845956775,"predicted_n":1,"predicted_ms":0.001,"predicted_per_token_ms":0.001,"predicted_per_second":1000000.0}}

MVP/evidence/student-vulkan-no-host.log

@@ -0,0 +1,810 @@
+0.00.157.450 I cmn  common_param: common_params_print_info: build 10068 (571d0d540d) with GNU 16.1.1 for Linux x86_64 (debug)
+0.00.157.452 I cmn  common_param: common_params_print_info: verbosity = 2147483647 (adjust with the `-lv N` CLI arg)
+0.00.157.453 I cmn  common_param: device_info:
+0.00.157.538 I cmn  common_param:   - Vulkan0 : AMD Radeon RX 460 Graphics (RADV POLARIS11) (2048 MiB, 1355 MiB free)
+0.00.157.545 I cmn  common_param:   - CPU     : AMD Ryzen 5 3600 6-Core Processor (15914 MiB, 15914 MiB free)
+0.00.157.562 I cmn  common_param: system_info: n_threads = 6 (n_threads_batch = 6) / 12 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | OPENMP = 1 | REPACK = 1 | 
+0.00.157.593 I srv          init: running without SSL
+0.00.157.649 I srv          init: using 11 threads for HTTP server
+0.00.157.651 I srv          init: The UI is disabled
+0.00.157.652 I srv          init: Use --ui/--no-ui (or deprecated --webui/--no-webui) to enable/disable
+0.00.157.741 W srv  llama_server: -----------------
+0.00.157.742 W srv  llama_server: CORS is set to allow all origins ('*') and no API key is set
+0.00.157.742 W srv  llama_server: this can be a security risk (cross-origin attacks)
+0.00.157.743 W srv  llama_server: more info: https://github.com/ggml-org/llama.cpp/pull/25655
+0.00.157.743 W srv  llama_server: -----------------
+0.00.157.762 I srv         start: binding port with default address family
+0.00.158.926 I srv    load_model: loading model 'MVP/artifacts/qwen35-text-n4-bf16.gguf'
+0.00.158.928 I srv    load_model: local path 'MVP/artifacts/qwen35-text-n4-bf16.gguf'
+0.00.158.940 I cmn  common_init_: fitting params to device memory ...
+0.00.158.940 I cmn  common_init_: (for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on)
+0.00.158.941 I common_params_fit_impl: getting device memory data for initial parameters:
+0.00.221.086 I llama_model_loader: loaded meta data with 35 key-value pairs and 55 tensors from MVP/artifacts/qwen35-text-n4-bf16.gguf (version GGUF V3 (latest))
+0.00.221.097 I llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
+0.00.221.100 I llama_model_loader: - kv   0:                       general.architecture str              = qwen35
+0.00.221.100 I llama_model_loader: - kv   1:                               general.type str              = model
+0.00.221.101 I llama_model_loader: - kv   2:                               general.name str              = Qwen35 0.8b Text N4
+0.00.221.101 I llama_model_loader: - kv   3:                           general.finetune str              = 0.8b-text-n4
+0.00.221.101 I llama_model_loader: - kv   4:                           general.basename str              = qwen35
+0.00.221.102 I llama_model_loader: - kv   5:                         general.size_label str              = 337M
+0.00.221.103 I llama_model_loader: - kv   6:                         qwen35.block_count u32              = 4
+0.00.221.103 I llama_model_loader: - kv   7:                      qwen35.context_length u32              = 262144
+0.00.221.104 I llama_model_loader: - kv   8:                    qwen35.embedding_length u32              = 1024
+0.00.221.104 I llama_model_loader: - kv   9:                 qwen35.feed_forward_length u32              = 3584
+0.00.221.105 I llama_model_loader: - kv  10:                qwen35.attention.head_count u32              = 8
+0.00.221.105 I llama_model_loader: - kv  11:             qwen35.attention.head_count_kv u32              = 2
+0.00.221.111 I llama_model_loader: - kv  12:             qwen35.rope.dimension_sections arr[i32,4]       = [11, 11, 10, 0]
+0.00.221.116 I llama_model_loader: - kv  13:                      qwen35.rope.freq_base f32              = 10000000.000000
+0.00.221.117 I llama_model_loader: - kv  14:    qwen35.attention.layer_norm_rms_epsilon f32              = 0.000001
+0.00.221.117 I llama_model_loader: - kv  15:                qwen35.attention.key_length u32              = 256
+0.00.221.118 I llama_model_loader: - kv  16:              qwen35.attention.value_length u32              = 256
+0.00.221.118 I llama_model_loader: - kv  17:                          general.file_type u32              = 32
+0.00.221.119 I llama_model_loader: - kv  18:                     qwen35.ssm.conv_kernel u32              = 4
+0.00.221.119 I llama_model_loader: - kv  19:                      qwen35.ssm.state_size u32              = 128
+0.00.221.119 I llama_model_loader: - kv  20:                     qwen35.ssm.group_count u32              = 16
+0.00.221.120 I llama_model_loader: - kv  21:                  qwen35.ssm.time_step_rank u32              = 16
+0.00.221.120 I llama_model_loader: - kv  22:                      qwen35.ssm.inner_size u32              = 2048
+0.00.221.121 I llama_model_loader: - kv  23:             qwen35.full_attention_interval u32              = 4
+0.00.221.121 I llama_model_loader: - kv  24:                qwen35.rope.dimension_count u32              = 64
+0.00.221.121 I llama_model_loader: - kv  25:               general.quantization_version u32              = 2
+0.00.221.122 I llama_model_loader: - kv  26:                       tokenizer.ggml.model str              = gpt2
+0.00.221.122 I llama_model_loader: - kv  27:                         tokenizer.ggml.pre str              = qwen35
+0.00.244.538 I llama_model_loader: - kv  28:                      tokenizer.ggml.tokens arr[str,248320]  = ["!", "\"", "#", "$", "%", "&", "'", ...
+0.00.251.986 I llama_model_loader: - kv  29:                  tokenizer.ggml.token_type arr[i32,248320]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
+0.00.276.929 I llama_model_loader: - kv  30:                      tokenizer.ggml.merges arr[str,247587]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
+0.00.276.934 I llama_model_loader: - kv  31:                tokenizer.ggml.eos_token_id u32              = 248044
+0.00.276.935 I llama_model_loader: - kv  32:            tokenizer.ggml.padding_token_id u32              = 248044
+0.00.276.936 I llama_model_loader: - kv  33:               tokenizer.ggml.add_bos_token bool             = false
+0.00.276.938 I llama_model_loader: - kv  34:                    tokenizer.chat_template str              = {%- set image_count = namespace(value...
+0.00.276.939 I llama_model_loader: - type  f32:   23 tensors
+0.00.276.940 I llama_model_loader: - type bf16:   32 tensors
+0.00.276.941 I print_info: file format = GGUF V3 (latest)
+0.00.276.942 I print_info: file type   = BF16
+0.00.276.946 I print_info: file size   = 643.51 MiB (16.00 BPW) 
+0.00.277.092 I llama_prepare_model_devices: using device Vulkan0 (AMD Radeon RX 460 Graphics (RADV POLARIS11)) (0000:29:00.0) - 1355 MiB free
+0.00.412.969 D init_tokenizer: initializing tokenizer for type 2
+0.00.452.683 I load: 0 unused tokens
+0.00.452.696 D load: control token: 248075 '<tts_text_bos_single>' is not marked as EOG
+0.00.452.697 D load: control token: 248073 '<tts_text_bos>' is not marked as EOG
+0.00.452.697 D load: control token: 248072 '<tts_pad>' is not marked as EOG
+0.00.452.698 D load: control token: 248071 '<|audio_end|>' is not marked as EOG
+0.00.452.698 D load: control token: 248061 '<|fim_middle|>' is not marked as EOG
+0.00.452.699 D load: control token: 248055 '<|vision_pad|>' is not marked as EOG
+0.00.452.699 D load: control token: 248052 '<|quad_end|>' is not marked as EOG
+0.00.452.699 D load: control token: 248049 '<|box_start|>' is not marked as EOG
+0.00.452.700 D load: control token: 248048 '<|object_ref_end|>' is not marked as EOG
+0.00.452.701 D load: control token: 248045 '<|im_start|>' is not marked as EOG
+0.00.453.043 D load: control token: 248057 '<|video_pad|>' is not marked as EOG
+0.00.454.906 D load: control token: 248070 '<|audio_start|>' is not marked as EOG
+0.00.455.011 D load: control token: 248056 '<|image_pad|>' is not marked as EOG
+0.00.455.224 D load: control token: 248054 '<|vision_end|>' is not marked as EOG
+0.00.458.902 D load: control token: 248060 '<|fim_prefix|>' is not marked as EOG
+0.00.459.488 D load: control token: 248050 '<|box_end|>' is not marked as EOG
+0.00.465.147 D load: control token: 248074 '<tts_text_eod>' is not marked as EOG
+0.00.474.463 D load: control token: 248053 '<|vision_start|>' is not marked as EOG
+0.00.476.685 D load: control token: 248062 '<|fim_suffix|>' is not marked as EOG
+0.00.478.308 D load: control token: 248047 '<|object_ref_start|>' is not marked as EOG
+0.00.479.490 D load: control token: 248051 '<|quad_start|>' is not marked as EOG
+0.00.481.121 D load: control token: 248076 '<|audio_pad|>' is not marked as EOG
+0.00.498.805 I load: printing all EOG tokens:
+0.00.498.808 I load:   - 248044 ('<|endoftext|>')
+0.00.498.809 I load:   - 248046 ('<|im_end|>')
+0.00.498.809 I load:   - 248063 ('<|fim_pad|>')
+0.00.498.809 I load:   - 248064 ('<|repo_name|>')
+0.00.498.810 I load:   - 248065 ('<|file_sep|>')
+0.00.499.347 I load: special tokens cache size = 33
+0.00.583.646 I load: token to piece cache size = 1.7581 MB
+0.00.583.656 I print_info: arch                  = qwen35
+0.00.583.657 I print_info: vocab_only            = 0
+0.00.583.658 I print_info: no_alloc              = 1
+0.00.583.658 I print_info: n_ctx_train           = 262144
+0.00.583.659 I print_info: n_embd_inp            = 1024
+0.00.583.660 I print_info: n_embd                = 1024
+0.00.583.660 I print_info: n_embd_out            = 1024
+0.00.583.661 I print_info: n_layer               = 4
+0.00.583.661 I print_info: n_layer_all           = 4
+0.00.583.666 I print_info: n_head                = 8
+0.00.583.667 I print_info: n_head_kv             = 2
+0.00.583.667 I print_info: n_rot                 = 64
+0.00.583.668 I print_info: n_swa                 = 0
+0.00.583.668 I print_info: is_swa_any            = 0
+0.00.583.669 I print_info: n_embd_head_k         = 256
+0.00.583.669 I print_info: n_embd_head_v         = 256
+0.00.583.669 I print_info: n_gqa                 = 4
+0.00.583.670 I print_info: n_embd_k_gqa          = 512
+0.00.583.671 I print_info: n_embd_v_gqa          = 512
+0.00.583.672 I print_info: f_norm_eps            = 0.0e+00
+0.00.583.673 I print_info: f_norm_rms_eps        = 1.0e-06
+0.00.583.673 I print_info: f_clamp_kqv           = 0.0e+00
+0.00.583.673 I print_info: f_max_alibi_bias      = 0.0e+00
+0.00.583.673 I print_info: f_logit_scale         = 0.0e+00
+0.00.583.674 I print_info: f_attn_scale          = 0.0e+00
+0.00.583.674 I print_info: f_attn_value_scale    = 0.0000
+0.00.583.675 I print_info: n_ff                  = 3584
+0.00.583.675 I print_info: n_expert              = 0
+0.00.583.675 I print_info: n_expert_used         = 0
+0.00.583.675 I print_info: n_expert_groups       = 0
+0.00.583.675 I print_info: n_group_used          = 0
+0.00.583.676 I print_info: causal attn           = 1
+0.00.583.676 I print_info: pooling type          = -1
+0.00.583.676 I print_info: rope type             = 40
+0.00.583.676 I print_info: rope scaling          = linear
+0.00.583.678 I print_info: freq_base_train       = 10000000.0
+0.00.583.678 I print_info: freq_scale_train      = 1
+0.00.583.680 I print_info: n_ctx_orig_yarn       = 262144
+0.00.583.681 I print_info: rope_yarn_log_mul     = 0.0000
+0.00.583.681 I print_info: rope_finetuned        = unknown
+0.00.583.681 I print_info: mrope sections        = [11, 11, 10, 0]
+0.00.583.681 I print_info: ssm_d_conv            = 4
+0.00.583.682 I print_info: ssm_d_inner           = 2048
+0.00.583.682 I print_info: ssm_d_state           = 128
+0.00.583.682 I print_info: ssm_dt_rank           = 16
+0.00.583.682 I print_info: ssm_n_group           = 16
+0.00.583.683 I print_info: ssm_dt_b_c_rms        = 0
+0.00.583.683 I print_info: model type            = ?B
+0.00.583.684 I print_info: model params          = 337.30 M
+0.00.583.685 I print_info: general.name          = Qwen35 0.8b Text N4
+0.00.583.686 I print_info: vocab type            = BPE
+0.00.583.686 I print_info: n_vocab               = 248320
+0.00.583.686 I print_info: n_merges              = 247587
+0.00.583.686 I print_info: BOS token             = 11 ','
+0.00.583.687 I print_info: EOS token             = 248044 '<|endoftext|>'
+0.00.583.687 I print_info: EOT token             = 248046 '<|im_end|>'
+0.00.583.687 I print_info: PAD token             = 248044 '<|endoftext|>'
+0.00.583.688 I print_info: LF token              = 198 'Ċ'
+0.00.583.688 I print_info: FIM PRE token         = 248060 '<|fim_prefix|>'
+0.00.583.688 I print_info: FIM SUF token         = 248062 '<|fim_suffix|>'
+0.00.583.688 I print_info: FIM MID token         = 248061 '<|fim_middle|>'
+0.00.583.689 I print_info: FIM PAD token         = 248063 '<|fim_pad|>'
+0.00.583.689 I print_info: FIM REP token         = 248064 '<|repo_name|>'
+0.00.583.689 I print_info: FIM SEP token         = 248065 '<|file_sep|>'
+0.00.583.690 I print_info: EOG token             = 248044 '<|endoftext|>'
+0.00.583.690 I print_info: EOG token             = 248046 '<|im_end|>'
+0.00.583.690 I print_info: EOG token             = 248063 '<|fim_pad|>'
+0.00.583.691 I print_info: EOG token             = 248064 '<|repo_name|>'
+0.00.583.691 I print_info: EOG token             = 248065 '<|file_sep|>'
+0.00.583.691 I print_info: max token length      = 256
+0.00.583.692 I load_tensors: loading model tensors, this can take a while... (mmap = false, direct_io = false)
+0.00.595.805 D load_tensors: layer   0 assigned to device Vulkan0, is_swa = 0
+0.00.595.808 D load_tensors: layer   1 assigned to device Vulkan0, is_swa = 0
+0.00.595.808 D load_tensors: layer   2 assigned to device Vulkan0, is_swa = 0
+0.00.595.809 D load_tensors: layer   3 assigned to device Vulkan0, is_swa = 0
+0.00.595.809 D load_tensors: layer   4 assigned to device Vulkan0, is_swa = 0
+0.00.595.826 D create_tensor: loading tensor token_embd.weight
+0.00.595.832 D create_tensor: loading tensor output_norm.weight
+0.00.595.837 D create_tensor: loading tensor token_embd.weight
+0.00.595.842 D create_tensor: loading tensor blk.0.attn_norm.weight
+0.00.595.851 D create_tensor: loading tensor blk.0.post_attention_norm.weight
+0.00.595.855 D create_tensor: loading tensor blk.0.attn_qkv.weight
+0.00.595.862 D create_tensor: loading tensor blk.0.attn_gate.weight
+0.00.595.867 D create_tensor: loading tensor blk.0.ssm_conv1d.weight
+0.00.595.871 D create_tensor: loading tensor blk.0.ssm_dt.bias
+0.00.595.875 D create_tensor: loading tensor blk.0.ssm_a
+0.00.595.881 D create_tensor: loading tensor blk.0.ssm_beta.weight
+0.00.595.885 D create_tensor: loading tensor blk.0.ssm_alpha.weight
+0.00.595.889 D create_tensor: loading tensor blk.0.ssm_norm.weight
+0.00.595.894 D create_tensor: loading tensor blk.0.ssm_out.weight
+0.00.595.899 D create_tensor: loading tensor blk.0.ffn_gate.weight
+0.00.595.907 D create_tensor: loading tensor blk.0.ffn_down.weight
+0.00.595.911 D create_tensor: loading tensor blk.0.ffn_up.weight
+0.00.595.915 D create_tensor: loading tensor blk.1.attn_norm.weight
+0.00.595.920 D create_tensor: loading tensor blk.1.post_attention_norm.weight
+0.00.595.925 D create_tensor: loading tensor blk.1.attn_qkv.weight
+0.00.595.930 D create_tensor: loading tensor blk.1.attn_gate.weight
+0.00.595.938 D create_tensor: loading tensor blk.1.ssm_conv1d.weight
+0.00.595.943 D create_tensor: loading tensor blk.1.ssm_dt.bias
+0.00.595.948 D create_tensor: loading tensor blk.1.ssm_a
+0.00.595.952 D create_tensor: loading tensor blk.1.ssm_beta.weight
+0.00.595.956 D create_tensor: loading tensor blk.1.ssm_alpha.weight
+0.00.595.961 D create_tensor: loading tensor blk.1.ssm_norm.weight
+0.00.595.965 D create_tensor: loading tensor blk.1.ssm_out.weight
+0.00.595.974 D create_tensor: loading tensor blk.1.ffn_gate.weight
+0.00.595.978 D create_tensor: loading tensor blk.1.ffn_down.weight
+0.00.595.982 D create_tensor: loading tensor blk.1.ffn_up.weight
+0.00.595.987 D create_tensor: loading tensor blk.2.attn_norm.weight
+0.00.595.996 D create_tensor: loading tensor blk.2.post_attention_norm.weight
+0.00.596.001 D create_tensor: loading tensor blk.2.attn_qkv.weight
+0.00.596.007 D create_tensor: loading tensor blk.2.attn_gate.weight
+0.00.596.013 D create_tensor: loading tensor blk.2.ssm_conv1d.weight
+0.00.596.018 D create_tensor: loading tensor blk.2.ssm_dt.bias
+0.00.596.024 D create_tensor: loading tensor blk.2.ssm_a
+0.00.596.031 D create_tensor: loading tensor blk.2.ssm_beta.weight
+0.00.596.036 D create_tensor: loading tensor blk.2.ssm_alpha.weight
+0.00.596.040 D create_tensor: loading tensor blk.2.ssm_norm.weight
+0.00.596.046 D create_tensor: loading tensor blk.2.ssm_out.weight
+0.00.596.051 D create_tensor: loading tensor blk.2.ffn_gate.weight
+0.00.596.056 D create_tensor: loading tensor blk.2.ffn_down.weight
+0.00.596.061 D create_tensor: loading tensor blk.2.ffn_up.weight
+0.00.596.068 D create_tensor: loading tensor blk.3.attn_norm.weight
+0.00.596.073 D create_tensor: loading tensor blk.3.post_attention_norm.weight
+0.00.596.082 D create_tensor: loading tensor blk.3.attn_q.weight
+0.00.596.089 D create_tensor: loading tensor blk.3.attn_k.weight
+0.00.596.094 D create_tensor: loading tensor blk.3.attn_v.weight
+0.00.596.105 D create_tensor: loading tensor blk.3.attn_output.weight
+0.00.596.113 D create_tensor: loading tensor blk.3.attn_q_norm.weight
+0.00.596.118 D create_tensor: loading tensor blk.3.attn_k_norm.weight
+0.00.596.123 D create_tensor: loading tensor blk.3.ffn_gate.weight
+0.00.596.128 D create_tensor: loading tensor blk.3.ffn_down.weight
+0.00.596.135 D create_tensor: loading tensor blk.3.ffn_up.weight
+0.00.596.237 D done_getting_tensors: tensor 'token_embd.weight' (bf16) (and 0 others) cannot be used with preferred buffer type CPU_REPACK, using CPU instead
+0.00.596.316 I load_tensors: offloading output layer to GPU
+0.00.596.319 I load_tensors: offloading 3 repeating layers to GPU
+0.00.596.319 I load_tensors: offloaded 5/5 layers to GPU
+0.00.596.322 I load_tensors:          CPU model buffer size =     0.00 MiB
+0.00.596.323 I load_tensors:      Vulkan0 model buffer size =     0.00 MiB
+0.00.598.983 I llama_context: constructing llama_context
+0.00.598.987 I llama_context: n_seq_max     = 1
+0.00.598.987 I llama_context: n_ctx         = 256
+0.00.598.987 I llama_context: n_ctx_seq     = 256
+0.00.598.987 I llama_context: n_batch       = 128
+0.00.598.988 I llama_context: n_ubatch      = 1
+0.00.598.988 I llama_context: causal_attn   = 1
+0.00.598.988 I llama_context: flash_attn    = auto
+0.00.598.989 I llama_context: kv_unified    = false
+0.00.598.992 I llama_context: freq_base     = 10000000.0
+0.00.598.993 I llama_context: freq_scale    = 1
+0.00.598.993 I llama_context: n_rs_seq      = 0
+0.00.598.993 I llama_context: n_outputs_max = 1
+0.00.598.994 I llama_context: n_ctx_seq (256) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
+0.00.599.012 D set_abort_callback: call
+0.00.599.246 I llama_context: Vulkan_Host  output buffer size =     0.95 MiB
+0.00.599.261 D llama_kv_cache: layer   0: filtered
+0.00.599.262 D llama_kv_cache: layer   1: filtered
+0.00.599.262 D llama_kv_cache: layer   2: filtered
+0.00.599.264 D llama_kv_cache: layer   3: dev = Vulkan0
+0.00.599.273 I llama_kv_cache:    Vulkan0 KV buffer size =     0.00 MiB
+0.00.599.276 I llama_kv_cache: size =    0.50 MiB (   256 cells,   1 layers,  1/1 seqs), K (f16):    0.25 MiB, V (f16):    0.25 MiB
+0.00.599.277 I llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
+0.00.599.277 I llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
+0.00.599.279 D llama_memory_recurrent, layer   0: dev = Vulkan0
+0.00.599.281 D llama_memory_recurrent, layer   1: dev = Vulkan0
+0.00.599.281 D llama_memory_recurrent, layer   2: dev = Vulkan0
+0.00.599.282 D llama_memory_recurrent: layer   3: skipped
+0.00.600.725 I llama_memory_recurrent:    Vulkan0 RS buffer size =     3.21 MiB
+0.00.600.730 I llama_memory_recurrent: size =    3.21 MiB (     1 cells,   4 layers,  1 seqs  0 rs_seq), R (f32):    0.21 MiB, S (f32):    3.00 MiB
+0.00.600.730 D llama_context: enumerating backends
+0.00.600.733 D llama_context: backend_ptrs.size() = 2
+0.00.600.734 I sched_reserve: reserving ...
+0.00.600.734 D sched_reserve: max_nodes = 1792
+0.00.600.913 D sched_reserve: reserving full memory module
+0.00.600.916 D sched_reserve: worst-case: n_tokens = 1, n_seqs = 1, n_outputs = 1
+0.00.600.917 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.601.176 I resolve_fused_ops: Flash Attention enabled
+0.00.601.178 I resolve_fused_ops: resolving fused Gated Delta Net support:
+0.00.601.179 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.601.373 I resolve_fused_ops: fused Gated Delta Net (autoregressive) enabled
+0.00.601.375 D graph_reserve: reserving a graph for ubatch with n_tokens =   16, n_seqs =  1, n_outputs =   16
+0.00.601.569 I resolve_fused_ops: fused Gated Delta Net (chunked) enabled
+0.00.601.571 I resolve_fused_ops: resolving fused Lightning Indexer support:
+0.00.601.571 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.601.753 I resolve_fused_ops: Lightning Indexer enabled
+0.00.601.754 I resolve_fused_ops: resolving fused DeepSeek V4 HC support:
+0.00.601.755 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.601.934 I resolve_fused_ops: fused DeepSeek V4 HC pre enabled
+0.00.601.936 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.602.112 I resolve_fused_ops: fused DeepSeek V4 HC comb enabled
+0.00.602.114 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.602.289 I resolve_fused_ops: fused DeepSeek V4 HC post enabled
+0.00.602.290 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.602.544 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.602.727 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.602.904 I sched_reserve:    Vulkan0 compute buffer size =     2.12 MiB
+0.00.602.906 I sched_reserve: Vulkan_Host compute buffer size =     0.04 MiB
+0.00.602.907 I sched_reserve: graph nodes  = 235
+0.00.602.907 I sched_reserve: graph splits = 2
+0.00.602.907 I sched_reserve: reserve took 2.17 ms, sched copies = 1
+0.00.603.133 I common_memory_breakdown_print: | memory breakdown [MiB]                         | total   free    self   model   context   compute    unaccounted |
+0.00.603.134 I common_memory_breakdown_print: |   - Vulkan0 (RX 460 Graphics (RADV POLARIS11)) |  2048 = 1351 + ( 649 =   643 +       3 +       2) +          47 |
+0.00.603.135 I common_memory_breakdown_print: |   - Host                                       |                  485 =   485 +       0 +       0                |
+0.00.640.943 I common_params_fit_impl: projected to use 649 MiB of device memory vs. 1351 MiB of free device memory
+0.00.640.946 I common_params_fit_impl: cannot meet free memory target of 1024 MiB, need to reduce device memory by 321 MiB
+0.00.640.947 I common_params_fit_impl: context size set by user to 256 -> no change
+0.00.640.997 W common_fit_params: failed to fit params to free device memory: n_gpu_layers already set by user to -2, abort
+0.00.641.005 I common_fit_params: fitting params to free memory took 0.48 seconds
+0.00.674.621 I llama_model_loader: loaded meta data with 35 key-value pairs and 55 tensors from MVP/artifacts/qwen35-text-n4-bf16.gguf (version GGUF V3 (latest))
+0.00.674.629 I llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
+0.00.674.632 I llama_model_loader: - kv   0:                       general.architecture str              = qwen35
+0.00.674.633 I llama_model_loader: - kv   1:                               general.type str              = model
+0.00.674.633 I llama_model_loader: - kv   2:                               general.name str              = Qwen35 0.8b Text N4
+0.00.674.635 I llama_model_loader: - kv   3:                           general.finetune str              = 0.8b-text-n4
+0.00.674.636 I llama_model_loader: - kv   4:                           general.basename str              = qwen35
+0.00.674.636 I llama_model_loader: - kv   5:                         general.size_label str              = 337M
+0.00.674.638 I llama_model_loader: - kv   6:                         qwen35.block_count u32              = 4
+0.00.674.638 I llama_model_loader: - kv   7:                      qwen35.context_length u32              = 262144
+0.00.674.639 I llama_model_loader: - kv   8:                    qwen35.embedding_length u32              = 1024
+0.00.674.639 I llama_model_loader: - kv   9:                 qwen35.feed_forward_length u32              = 3584
+0.00.674.640 I llama_model_loader: - kv  10:                qwen35.attention.head_count u32              = 8
+0.00.674.640 I llama_model_loader: - kv  11:             qwen35.attention.head_count_kv u32              = 2
+0.00.674.648 I llama_model_loader: - kv  12:             qwen35.rope.dimension_sections arr[i32,4]       = [11, 11, 10, 0]
+0.00.674.654 I llama_model_loader: - kv  13:                      qwen35.rope.freq_base f32              = 10000000.000000
+0.00.674.655 I llama_model_loader: - kv  14:    qwen35.attention.layer_norm_rms_epsilon f32              = 0.000001
+0.00.674.656 I llama_model_loader: - kv  15:                qwen35.attention.key_length u32              = 256
+0.00.674.656 I llama_model_loader: - kv  16:              qwen35.attention.value_length u32              = 256
+0.00.674.657 I llama_model_loader: - kv  17:                          general.file_type u32              = 32
+0.00.674.657 I llama_model_loader: - kv  18:                     qwen35.ssm.conv_kernel u32              = 4
+0.00.674.659 I llama_model_loader: - kv  19:                      qwen35.ssm.state_size u32              = 128
+0.00.674.660 I llama_model_loader: - kv  20:                     qwen35.ssm.group_count u32              = 16
+0.00.674.660 I llama_model_loader: - kv  21:                  qwen35.ssm.time_step_rank u32              = 16
+0.00.674.661 I llama_model_loader: - kv  22:                      qwen35.ssm.inner_size u32              = 2048
+0.00.674.661 I llama_model_loader: - kv  23:             qwen35.full_attention_interval u32              = 4
+0.00.674.662 I llama_model_loader: - kv  24:                qwen35.rope.dimension_count u32              = 64
+0.00.674.662 I llama_model_loader: - kv  25:               general.quantization_version u32              = 2
+0.00.674.663 I llama_model_loader: - kv  26:                       tokenizer.ggml.model str              = gpt2
+0.00.674.663 I llama_model_loader: - kv  27:                         tokenizer.ggml.pre str              = qwen35
+0.00.698.130 I llama_model_loader: - kv  28:                      tokenizer.ggml.tokens arr[str,248320]  = ["!", "\"", "#", "$", "%", "&", "'", ...
+0.00.704.824 I llama_model_loader: - kv  29:                  tokenizer.ggml.token_type arr[i32,248320]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
+0.00.733.720 I llama_model_loader: - kv  30:                      tokenizer.ggml.merges arr[str,247587]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
+0.00.733.726 I llama_model_loader: - kv  31:                tokenizer.ggml.eos_token_id u32              = 248044
+0.00.733.726 I llama_model_loader: - kv  32:            tokenizer.ggml.padding_token_id u32              = 248044
+0.00.733.727 I llama_model_loader: - kv  33:               tokenizer.ggml.add_bos_token bool             = false
+0.00.733.731 I llama_model_loader: - kv  34:                    tokenizer.chat_template str              = {%- set image_count = namespace(value...
+0.00.733.733 I llama_model_loader: - type  f32:   23 tensors
+0.00.733.734 I llama_model_loader: - type bf16:   32 tensors
+0.00.733.735 I print_info: file format = GGUF V3 (latest)
+0.00.733.736 I print_info: file type   = BF16
+0.00.733.742 I print_info: file size   = 643.51 MiB (16.00 BPW) 
+0.00.733.877 I llama_prepare_model_devices: using device Vulkan0 (AMD Radeon RX 460 Graphics (RADV POLARIS11)) (0000:29:00.0) - 1354 MiB free
+0.00.861.552 D init_tokenizer: initializing tokenizer for type 2
+0.00.898.349 I load: 0 unused tokens
+0.00.898.363 D load: control token: 248075 '<tts_text_bos_single>' is not marked as EOG
+0.00.898.363 D load: control token: 248073 '<tts_text_bos>' is not marked as EOG
+0.00.898.364 D load: control token: 248072 '<tts_pad>' is not marked as EOG
+0.00.898.364 D load: control token: 248071 '<|audio_end|>' is not marked as EOG
+0.00.898.365 D load: control token: 248061 '<|fim_middle|>' is not marked as EOG
+0.00.898.365 D load: control token: 248055 '<|vision_pad|>' is not marked as EOG
+0.00.898.366 D load: control token: 248052 '<|quad_end|>' is not marked as EOG
+0.00.898.366 D load: control token: 248049 '<|box_start|>' is not marked as EOG
+0.00.898.367 D load: control token: 248048 '<|object_ref_end|>' is not marked as EOG
+0.00.898.368 D load: control token: 248045 '<|im_start|>' is not marked as EOG
+0.00.898.731 D load: control token: 248057 '<|video_pad|>' is not marked as EOG
+0.00.900.632 D load: control token: 248070 '<|audio_start|>' is not marked as EOG
+0.00.900.745 D load: control token: 248056 '<|image_pad|>' is not marked as EOG
+0.00.900.956 D load: control token: 248054 '<|vision_end|>' is not marked as EOG
+0.00.905.006 D load: control token: 248060 '<|fim_prefix|>' is not marked as EOG
+0.00.905.559 D load: control token: 248050 '<|box_end|>' is not marked as EOG
+0.00.911.601 D load: control token: 248074 '<tts_text_eod>' is not marked as EOG
+0.00.920.461 D load: control token: 248053 '<|vision_start|>' is not marked as EOG
+0.00.922.830 D load: control token: 248062 '<|fim_suffix|>' is not marked as EOG
+0.00.924.576 D load: control token: 248047 '<|object_ref_start|>' is not marked as EOG
+0.00.925.842 D load: control token: 248051 '<|quad_start|>' is not marked as EOG
+0.00.927.596 D load: control token: 248076 '<|audio_pad|>' is not marked as EOG
+0.00.946.598 I load: printing all EOG tokens:
+0.00.946.602 I load:   - 248044 ('<|endoftext|>')
+0.00.946.603 I load:   - 248046 ('<|im_end|>')
+0.00.946.603 I load:   - 248063 ('<|fim_pad|>')
+0.00.946.603 I load:   - 248064 ('<|repo_name|>')
+0.00.946.603 I load:   - 248065 ('<|file_sep|>')
+0.00.947.127 I load: special tokens cache size = 33
+0.01.032.072 I load: token to piece cache size = 1.7581 MB
+0.01.032.087 I print_info: arch                  = qwen35
+0.01.032.087 I print_info: vocab_only            = 0
+0.01.032.088 I print_info: no_alloc              = 0
+0.01.032.088 I print_info: n_ctx_train           = 262144
+0.01.032.089 I print_info: n_embd_inp            = 1024
+0.01.032.089 I print_info: n_embd                = 1024
+0.01.032.089 I print_info: n_embd_out            = 1024
+0.01.032.090 I print_info: n_layer               = 4
+0.01.032.090 I print_info: n_layer_all           = 4
+0.01.032.096 I print_info: n_head                = 8
+0.01.032.097 I print_info: n_head_kv             = 2
+0.01.032.098 I print_info: n_rot                 = 64
+0.01.032.098 I print_info: n_swa                 = 0
+0.01.032.099 I print_info: is_swa_any            = 0
+0.01.032.099 I print_info: n_embd_head_k         = 256
+0.01.032.099 I print_info: n_embd_head_v         = 256
+0.01.032.100 I print_info: n_gqa                 = 4
+0.01.032.101 I print_info: n_embd_k_gqa          = 512
+0.01.032.101 I print_info: n_embd_v_gqa          = 512
+0.01.032.102 I print_info: f_norm_eps            = 0.0e+00
+0.01.032.104 I print_info: f_norm_rms_eps        = 1.0e-06
+0.01.032.104 I print_info: f_clamp_kqv           = 0.0e+00
+0.01.032.104 I print_info: f_max_alibi_bias      = 0.0e+00
+0.01.032.105 I print_info: f_logit_scale         = 0.0e+00
+0.01.032.105 I print_info: f_attn_scale          = 0.0e+00
+0.01.032.106 I print_info: f_attn_value_scale    = 0.0000
+0.01.032.107 I print_info: n_ff                  = 3584
+0.01.032.107 I print_info: n_expert              = 0
+0.01.032.107 I print_info: n_expert_used         = 0
+0.01.032.107 I print_info: n_expert_groups       = 0
+0.01.032.108 I print_info: n_group_used          = 0
+0.01.032.108 I print_info: causal attn           = 1
+0.01.032.108 I print_info: pooling type          = -1
+0.01.032.109 I print_info: rope type             = 40
+0.01.032.109 I print_info: rope scaling          = linear
+0.01.032.110 I print_info: freq_base_train       = 10000000.0
+0.01.032.111 I print_info: freq_scale_train      = 1
+0.01.032.111 I print_info: n_ctx_orig_yarn       = 262144
+0.01.032.112 I print_info: rope_yarn_log_mul     = 0.0000
+0.01.032.112 I print_info: rope_finetuned        = unknown
+0.01.032.112 I print_info: mrope sections        = [11, 11, 10, 0]
+0.01.032.113 I print_info: ssm_d_conv            = 4
+0.01.032.113 I print_info: ssm_d_inner           = 2048
+0.01.032.113 I print_info: ssm_d_state           = 128
+0.01.032.114 I print_info: ssm_dt_rank           = 16
+0.01.032.114 I print_info: ssm_n_group           = 16
+0.01.032.114 I print_info: ssm_dt_b_c_rms        = 0
+0.01.032.115 I print_info: model type            = ?B
+0.01.032.115 I print_info: model params          = 337.30 M
+0.01.032.116 I print_info: general.name          = Qwen35 0.8b Text N4
+0.01.032.117 I print_info: vocab type            = BPE
+0.01.032.118 I print_info: n_vocab               = 248320
+0.01.032.118 I print_info: n_merges              = 247587
+0.01.032.119 I print_info: BOS token             = 11 ','
+0.01.032.119 I print_info: EOS token             = 248044 '<|endoftext|>'
+0.01.032.119 I print_info: EOT token             = 248046 '<|im_end|>'
+0.01.032.120 I print_info: PAD token             = 248044 '<|endoftext|>'
+0.01.032.120 I print_info: LF token              = 198 'Ċ'
+0.01.032.121 I print_info: FIM PRE token         = 248060 '<|fim_prefix|>'
+0.01.032.121 I print_info: FIM SUF token         = 248062 '<|fim_suffix|>'
+0.01.032.121 I print_info: FIM MID token         = 248061 '<|fim_middle|>'
+0.01.032.121 I print_info: FIM PAD token         = 248063 '<|fim_pad|>'
+0.01.032.122 I print_info: FIM REP token         = 248064 '<|repo_name|>'
+0.01.032.122 I print_info: FIM SEP token         = 248065 '<|file_sep|>'
+0.01.032.122 I print_info: EOG token             = 248044 '<|endoftext|>'
+0.01.032.123 I print_info: EOG token             = 248046 '<|im_end|>'
+0.01.032.123 I print_info: EOG token             = 248063 '<|fim_pad|>'
+0.01.032.123 I print_info: EOG token             = 248064 '<|repo_name|>'
+0.01.032.124 I print_info: EOG token             = 248065 '<|file_sep|>'
+0.01.032.124 I print_info: max token length      = 256
+0.01.032.125 I load_tensors: loading model tensors, this can take a while... (mmap = true, direct_io = false)
+0.01.032.304 D load_tensors: layer   0 assigned to device Vulkan0, is_swa = 0
+0.01.032.307 D load_tensors: layer   1 assigned to device Vulkan0, is_swa = 0
+0.01.032.307 D load_tensors: layer   2 assigned to device Vulkan0, is_swa = 0
+0.01.032.307 D load_tensors: layer   3 assigned to device Vulkan0, is_swa = 0
+0.01.032.308 D load_tensors: layer   4 assigned to device Vulkan0, is_swa = 0
+0.01.032.327 D create_tensor: loading tensor token_embd.weight
+0.01.032.336 D create_tensor: loading tensor output_norm.weight
+0.01.032.344 D create_tensor: loading tensor token_embd.weight
+0.01.032.350 D create_tensor: loading tensor blk.0.attn_norm.weight
+0.01.032.357 D create_tensor: loading tensor blk.0.post_attention_norm.weight
+0.01.032.363 D create_tensor: loading tensor blk.0.attn_qkv.weight
+0.01.032.369 D create_tensor: loading tensor blk.0.attn_gate.weight
+0.01.032.377 D create_tensor: loading tensor blk.0.ssm_conv1d.weight
+0.01.032.390 D create_tensor: loading tensor blk.0.ssm_dt.bias
+0.01.032.395 D create_tensor: loading tensor blk.0.ssm_a
+0.01.032.402 D create_tensor: loading tensor blk.0.ssm_beta.weight
+0.01.032.407 D create_tensor: loading tensor blk.0.ssm_alpha.weight
+0.01.032.412 D create_tensor: loading tensor blk.0.ssm_norm.weight
+0.01.032.418 D create_tensor: loading tensor blk.0.ssm_out.weight
+0.01.032.425 D create_tensor: loading tensor blk.0.ffn_gate.weight
+0.01.032.430 D create_tensor: loading tensor blk.0.ffn_down.weight
+0.01.032.435 D create_tensor: loading tensor blk.0.ffn_up.weight
+0.01.032.443 D create_tensor: loading tensor blk.1.attn_norm.weight
+0.01.032.450 D create_tensor: loading tensor blk.1.post_attention_norm.weight
+0.01.032.457 D create_tensor: loading tensor blk.1.attn_qkv.weight
+0.01.032.463 D create_tensor: loading tensor blk.1.attn_gate.weight
+0.01.032.471 D create_tensor: loading tensor blk.1.ssm_conv1d.weight
+0.01.032.477 D create_tensor: loading tensor blk.1.ssm_dt.bias
+0.01.032.483 D create_tensor: loading tensor blk.1.ssm_a
+0.01.032.489 D create_tensor: loading tensor blk.1.ssm_beta.weight
+0.01.032.495 D create_tensor: loading tensor blk.1.ssm_alpha.weight
+0.01.032.501 D create_tensor: loading tensor blk.1.ssm_norm.weight
+0.01.032.507 D create_tensor: loading tensor blk.1.ssm_out.weight
+0.01.032.513 D create_tensor: loading tensor blk.1.ffn_gate.weight
+0.01.032.519 D create_tensor: loading tensor blk.1.ffn_down.weight
+0.01.032.527 D create_tensor: loading tensor blk.1.ffn_up.weight
+0.01.032.533 D create_tensor: loading tensor blk.2.attn_norm.weight
+0.01.032.541 D create_tensor: loading tensor blk.2.post_attention_norm.weight
+0.01.032.548 D create_tensor: loading tensor blk.2.attn_qkv.weight
+0.01.032.555 D create_tensor: loading tensor blk.2.attn_gate.weight
+0.01.032.561 D create_tensor: loading tensor blk.2.ssm_conv1d.weight
+0.01.032.569 D create_tensor: loading tensor blk.2.ssm_dt.bias
+0.01.032.575 D create_tensor: loading tensor blk.2.ssm_a
+0.01.032.582 D create_tensor: loading tensor blk.2.ssm_beta.weight
+0.01.032.587 D create_tensor: loading tensor blk.2.ssm_alpha.weight
+0.01.032.594 D create_tensor: loading tensor blk.2.ssm_norm.weight
+0.01.032.600 D create_tensor: loading tensor blk.2.ssm_out.weight
+0.01.032.607 D create_tensor: loading tensor blk.2.ffn_gate.weight
+0.01.032.613 D create_tensor: loading tensor blk.2.ffn_down.weight
+0.01.032.619 D create_tensor: loading tensor blk.2.ffn_up.weight
+0.01.032.625 D create_tensor: loading tensor blk.3.attn_norm.weight
+0.01.032.633 D create_tensor: loading tensor blk.3.post_attention_norm.weight
+0.01.032.643 D create_tensor: loading tensor blk.3.attn_q.weight
+0.01.032.654 D create_tensor: loading tensor blk.3.attn_k.weight
+0.01.032.661 D create_tensor: loading tensor blk.3.attn_v.weight
+0.01.032.677 D create_tensor: loading tensor blk.3.attn_output.weight
+0.01.032.684 D create_tensor: loading tensor blk.3.attn_q_norm.weight
+0.01.032.691 D create_tensor: loading tensor blk.3.attn_k_norm.weight
+0.01.032.697 D create_tensor: loading tensor blk.3.ffn_gate.weight
+0.01.032.704 D create_tensor: loading tensor blk.3.ffn_down.weight
+0.01.032.710 D create_tensor: loading tensor blk.3.ffn_up.weight
+0.01.032.831 D done_getting_tensors: tensor 'token_embd.weight' (bf16) (and 0 others) cannot be used with preferred buffer type CPU_REPACK, using CPU instead
+0.01.490.415 I load_tensors: offloading output layer to GPU
+0.01.490.417 I load_tensors: offloading 3 repeating layers to GPU
+0.01.490.417 I load_tensors: offloaded 5/5 layers to GPU
+0.01.490.422 I load_tensors:   CPU_Mapped model buffer size =   485.00 MiB
+0.01.490.423 I load_tensors:      Vulkan0 model buffer size =   643.51 MiB
+0.01.693.663 I cmn  common_init_: added <|endoftext|> logit bias = -inf
+0.01.693.667 I cmn  common_init_: added <|im_end|> logit bias = -inf
+0.01.693.667 I cmn  common_init_: added <|fim_pad|> logit bias = -inf
+0.01.693.667 I cmn  common_init_: added <|repo_name|> logit bias = -inf
+0.01.693.668 I cmn  common_init_: added <|file_sep|> logit bias = -inf
+0.01.693.716 I llama_context: constructing llama_context
+0.01.693.719 I llama_context: n_seq_max     = 1
+0.01.693.719 I llama_context: n_ctx         = 256
+0.01.693.719 I llama_context: n_ctx_seq     = 256
+0.01.693.720 I llama_context: n_batch       = 128
+0.01.693.720 I llama_context: n_ubatch      = 1
+0.01.693.720 I llama_context: causal_attn   = 1
+0.01.693.721 I llama_context: flash_attn    = auto
+0.01.693.721 I llama_context: kv_unified    = false
+0.01.693.726 I llama_context: freq_base     = 10000000.0
+0.01.693.726 I llama_context: freq_scale    = 1
+0.01.693.727 I llama_context: n_rs_seq      = 0
+0.01.693.727 I llama_context: n_outputs_max = 1
+0.01.693.727 I llama_context: n_ctx_seq (256) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
+0.01.693.752 D set_abort_callback: call
+0.01.694.025 I llama_context: Vulkan_Host  output buffer size =     0.95 MiB
+0.01.694.036 D llama_kv_cache: layer   0: filtered
+0.01.694.036 D llama_kv_cache: layer   1: filtered
+0.01.694.036 D llama_kv_cache: layer   2: filtered
+0.01.694.038 D llama_kv_cache: layer   3: dev = Vulkan0
+0.01.694.075 I llama_kv_cache:    Vulkan0 KV buffer size =     0.50 MiB
+0.01.694.216 I llama_kv_cache: size =    0.50 MiB (   256 cells,   1 layers,  1/1 seqs), K (f16):    0.25 MiB, V (f16):    0.25 MiB
+0.01.694.219 I llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
+0.01.694.219 I llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
+0.01.694.222 D llama_memory_recurrent, layer   0: dev = Vulkan0
+0.01.694.224 D llama_memory_recurrent, layer   1: dev = Vulkan0
+0.01.694.225 D llama_memory_recurrent, layer   2: dev = Vulkan0
+0.01.694.226 D llama_memory_recurrent: layer   3: skipped
+0.01.694.420 I llama_memory_recurrent:    Vulkan0 RS buffer size =     3.21 MiB
+0.01.694.425 I llama_memory_recurrent: size =    3.21 MiB (     1 cells,   4 layers,  1 seqs  0 rs_seq), R (f32):    0.21 MiB, S (f32):    3.00 MiB
+0.01.694.426 D llama_context: enumerating backends
+0.01.694.428 D llama_context: backend_ptrs.size() = 2
+0.01.694.430 I sched_reserve: reserving ...
+0.01.694.430 D sched_reserve: max_nodes = 1792
+0.01.694.604 D sched_reserve: reserving full memory module
+0.01.694.607 D sched_reserve: worst-case: n_tokens = 1, n_seqs = 1, n_outputs = 1
+0.01.694.608 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.694.900 I resolve_fused_ops: Flash Attention enabled
+0.01.694.903 I resolve_fused_ops: resolving fused Gated Delta Net support:
+0.01.694.904 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.695.149 I resolve_fused_ops: fused Gated Delta Net (autoregressive) enabled
+0.01.695.152 D graph_reserve: reserving a graph for ubatch with n_tokens =   16, n_seqs =  1, n_outputs =   16
+0.01.695.352 I resolve_fused_ops: fused Gated Delta Net (chunked) enabled
+0.01.695.354 I resolve_fused_ops: resolving fused Lightning Indexer support:
+0.01.695.354 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.695.559 I resolve_fused_ops: Lightning Indexer enabled
+0.01.695.562 I resolve_fused_ops: resolving fused DeepSeek V4 HC support:
+0.01.695.563 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.695.776 I resolve_fused_ops: fused DeepSeek V4 HC pre enabled
+0.01.695.778 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.695.964 I resolve_fused_ops: fused DeepSeek V4 HC comb enabled
+0.01.695.967 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.696.413 I resolve_fused_ops: fused DeepSeek V4 HC post enabled
+0.01.696.416 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.696.819 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.697.172 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.697.437 I sched_reserve:    Vulkan0 compute buffer size =     2.12 MiB
+0.01.697.440 I sched_reserve: Vulkan_Host compute buffer size =     0.04 MiB
+0.01.697.440 I sched_reserve: graph nodes  = 235
+0.01.697.440 I sched_reserve: graph splits = 2
+0.01.697.441 I sched_reserve: reserve took 3.01 ms, sched copies = 1
+0.01.697.577 D set_adapters_lora: adapters = (nil)
+0.01.697.579 D adapters_lora_are_same: adapters = (nil)
+0.01.697.580 I cmn  common_init_: warming up the model with an empty run - please wait ... (--no-warmup to disable)
+0.01.728.489 I cmn  common_conte: the context does not support partial sequence removal
+0.01.736.371 I srv    load_model: speculative decoding will use checkpoints
+0.01.736.374 I srv    load_model: initializing, n_slots = 1, n_ctx_slot = 256, kv_unified = 'false'
+0.01.736.376 I spec common_specu: no implementations specified for speculative decoding
+0.01.736.377 I slot   load_model: id  0 | task -1 | new slot, n_ctx = 256
+0.01.736.377 D slot        reset: id  0 | task -1 | 
+0.01.736.394 I srv    load_model: prompt cache is enabled, size limit: 8192 MiB
+0.01.736.395 I srv    load_model: use `--cache-ram 0` to disable the prompt cache
+0.01.736.395 I srv    load_model: for more info see https://github.com/ggml-org/llama.cpp/pull/16391
+0.01.736.396 I srv    load_model: context checkpoints enabled, max = 32, min spacing = 8192
+0.01.736.410 I srv          init: idle slots will be saved to prompt cache upon starting a new task
+0.01.736.410 D srv          init: __TEST_TAG_CACHE_IDLE_SLOTS_ENABLED__
+0.01.739.633 D common_chat_templates_apply_jinja: using differential autoparser
+0.01.739.637 D === Starting differential analysis ===
+0.01.739.637 D Phase 1: Reasoning analysis
+0.01.742.018 D Phase 2: Content analysis
+0.01.743.753 D Phase 3: Tool call analysis
+0.01.745.870 D Phase 3a: Function call analysis
+0.01.746.912 D Phase 3b: Argument analysis
+0.01.751.030 D Phase 3c: Call id analysis
+0.01.753.273 D Template application failed: 
+------------
+While executing CallExpression at line 43, column 24 in source:
+... not messages %}↵    {{- raise_exception('No messages provided.') }}↵{%- endif %...
+                                           ^
+Error: Jinja Exception: No messages provided.
+0.01.753.516 D detect_user_start_marker: Template application failed, unsupported empty messages? trying complex variant
+0.01.754.234 D 
+--- Reasoning & Content Structure ---
+0.01.754.237 D user_msg_start: <|im_start|>user
+0.01.754.238 D assistant_msg_start: <|im_start|>assistant
+0.01.754.239 D reasoning_mode: TAG_BASED
+0.01.754.239 D reasoning_start: '<think>
+'
+0.01.754.239 D reasoning_end: '
+</think>
+
+'
+0.01.754.240 D content_mode: PLAIN
+0.01.754.240 D content_start: ''
+0.01.754.240 D content_end: ''
+0.01.754.240 D 
+--- Tool Call Structure ---
+0.01.754.241 D tool_mode: TAG_WITH_TAGGED
+0.01.754.241 D supports_tools: true
+0.01.754.241 D supports_parallel_calls: true
+0.01.754.241 D tool_section_start: ''
+0.01.754.242 D tool_section_end: ''
+0.01.754.242 D per_call_start: '<tool_call>
+'
+0.01.754.242 D per_call_end: '</tool_call>'
+0.01.754.242 D func_name_prefix: '<function='
+0.01.754.242 D func_name_suffix: '>
+'
+0.01.754.242 D func_args_separator: ''
+0.01.754.243 D func_close: '</function>
+'
+0.01.754.243 D call_id_prefix: ''
+0.01.754.243 D call_id_suffix: ''
+0.01.754.243 D call_id_pos: 'NONE'
+0.01.754.244 D args_start: ''
+0.01.754.244 D args_end: ''
+0.01.754.245 D arg_name_prefix: '<parameter='
+0.01.754.245 D arg_name_suffix: '>
+'
+0.01.754.245 D arg_value_prefix: ''
+0.01.754.245 D arg_value_suffix: '
+</parameter>
+'
+0.01.754.246 D name_field: 'name'
+0.01.754.246 D args_field: 'arguments'
+0.01.754.246 D id_field: ''
+0.01.754.246 D gen_id_field: ''
+0.01.754.246 D parameter_order: ''
+0.01.754.246 D === Differential analysis complete ===
+0.01.755.841 D common_chat_templates_apply_jinja: generated parser:
+Sequence(Literal(<|im_start|>assistant
+), Space, Epsilon, Tag(content, Until()), End)
+
+parser generation prompt: <|im_start|>assistant
+<think>
+
+0.01.755.851 I srv          init: init: chat template, example_format: '<|im_start|>system
+You are a helpful assistant<|im_end|>
+<|im_start|>user
+Hello<|im_end|>
+<|im_start|>assistant
+Hi there<|im_end|>
+<|im_start|>user
+How are you?<|im_end|>
+<|im_start|>assistant
+<think>
+'
+0.01.755.908 D common_chat_templates_apply_jinja: using differential autoparser
+0.01.755.910 D === Starting differential analysis ===
+0.01.755.911 D Phase 1: Reasoning analysis
+0.01.757.804 D Phase 2: Content analysis
+0.01.759.520 D Phase 3: Tool call analysis
+0.01.761.815 D Phase 3a: Function call analysis
+0.01.762.894 D Phase 3b: Argument analysis
+0.01.767.699 D Phase 3c: Call id analysis
+0.01.769.326 D Template application failed: 
+------------
+While executing CallExpression at line 43, column 24 in source:
+... not messages %}↵    {{- raise_exception('No messages provided.') }}↵{%- endif %...
+                                           ^
+Error: Jinja Exception: No messages provided.
+0.01.769.533 D detect_user_start_marker: Template application failed, unsupported empty messages? trying complex variant
+0.01.770.201 D 
+--- Reasoning & Content Structure ---
+0.01.770.203 D user_msg_start: <|im_start|>user
+0.01.770.203 D assistant_msg_start: <|im_start|>assistant
+0.01.770.203 D reasoning_mode: TAG_BASED
+0.01.770.204 D reasoning_start: '<think>
+'
+0.01.770.204 D reasoning_end: '
+</think>
+
+'
+0.01.770.204 D content_mode: PLAIN
+0.01.770.204 D content_start: ''
+0.01.770.204 D content_end: ''
+0.01.770.205 D 
+--- Tool Call Structure ---
+0.01.770.205 D tool_mode: TAG_WITH_TAGGED
+0.01.770.205 D supports_tools: true
+0.01.770.205 D supports_parallel_calls: true
+0.01.770.205 D tool_section_start: ''
+0.01.770.206 D tool_section_end: ''
+0.01.770.206 D per_call_start: '<tool_call>
+'
+0.01.770.206 D per_call_end: '</tool_call>'
+0.01.770.206 D func_name_prefix: '<function='
+0.01.770.206 D func_name_suffix: '>
+'
+0.01.770.206 D func_args_separator: ''
+0.01.770.206 D func_close: '</function>
+'
+0.01.770.207 D call_id_prefix: ''
+0.01.770.207 D call_id_suffix: ''
+0.01.770.207 D call_id_pos: 'NONE'
+0.01.770.207 D args_start: ''
+0.01.770.208 D args_end: ''
+0.01.770.208 D arg_name_prefix: '<parameter='
+0.01.770.209 D arg_name_suffix: '>
+'
+0.01.770.210 D arg_value_prefix: ''
+0.01.770.210 D arg_value_suffix: '
+</parameter>
+'
+0.01.770.210 D name_field: 'name'
+0.01.770.210 D args_field: 'arguments'
+0.01.770.210 D id_field: ''
+0.01.770.210 D gen_id_field: ''
+0.01.770.210 D parameter_order: ''
+0.01.770.211 D === Differential analysis complete ===
+0.01.771.101 D common_chat_templates_apply_jinja: generated parser:
+Sequence(Literal(<|im_start|>assistant
+), Space, Repetition(Sequence(Epsilon, Literal(<think>), Repetition(Literal(
+), 0, 1), Tag(reasoning, Until(</think>)), Epsilon, Repetition(Literal(
+), 0, 1), Literal(</think>), Repetition(Literal(
+), 0, 1), Repetition(Literal(
+), 0, 1)), 0, 1), Tag(content, Until()), End)
+
+parser generation prompt: <|im_start|>assistant
+<think>
+
+0.01.771.108 I srv          init: init: chat template, thinking = 1
+0.01.771.124 I srv  llama_server: model loaded
+0.01.771.130 I srv  llama_server: listening on http://127.0.0.1:60909
+0.01.771.134 D que    start_loop: processing new tasks
+0.01.771.134 D que    start_loop: update slots
+0.01.771.135 I srv  update_slots: all slots are idle
+0.01.771.136 D que    start_loop: waiting for new tasks
+0.05.502.887 D srv          stop: all tasks already finished, no need to cancel
+0.12.097.342 I srv  server_strea: conv_id= (empty=1)
+0.12.097.400 D srv  eval_llama_c: reasoning budget: tokens=-1, generation_prompt='', start=0 toks, end=0 toks, forced=0 toks
+0.12.097.429 D res  add_waiting_: add task 0 to waiting list. current waiting = 0 (before add)
+0.12.097.430 D que          post: new task, id = 0/1, front = 0
+0.12.097.444 D que    start_loop: processing new tasks
+0.12.097.449 D que    start_loop: processing task, id = 0
+0.12.097.450 I slot get_availabl: id  0 | task -1 | selected slot by LRU, t_last = -1
+0.12.097.451 I srv  get_availabl: updating prompt cache
+0.12.097.455 I srv          load:  - looking for better prompt, base f_keep = -1.000, sim = 0.000
+0.12.097.458 I srv        update:  - cache state: 0 prompts, 0.000 MiB (limits: 8192.000 MiB, 256 tokens, 8589934592 est)
+0.12.097.459 I srv  get_availabl: prompt cache update took 0.01 ms
+0.12.097.470 D slot launch_slot_: id  0 | task -1 | launching slot : {"id":0,"n_ctx":256,"speculative":false,"is_processing":false}
+0.12.097.483 I slot launch_slot_: id  0 | task -1 | sampler chain: logits -> ?penalties -> ?dry -> ?top-n-sigma -> top-k -> ?typical -> top-p -> min-p -> ?xtc -> temp-ext -> dist 
+0.12.097.489 I slot launch_slot_: id  0 | task -1 | sampler params: 
+	repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
+	dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 256
+	top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.000
+	mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000, adaptive_target = -1.000, adaptive_decay = 0.900
+0.12.097.490 I slot launch_slot_: id  0 | task 0 | processing task, is_child = 0
+0.12.097.490 D que    start_loop: update slots
+0.12.097.491 D srv  update_slots: posting NEXT_RESPONSE
+0.12.097.492 D que          post: new task, id = 1, front = 0
+0.12.097.495 I slot   operator(): id  0 | task 0 | new prompt, n_ctx_slot = 256, n_keep = 0, task.n_tokens = 4
+0.12.097.497 I slot   operator(): id  0 | task 0 | cached n_tokens = 0, memory_seq_rm [0, end)
+0.12.097.505 I slot init_sampler: id  0 | task 0 | init sampler, took 0.00 ms, tokens: text = 4, total = 4
+0.12.097.509 D slot   operator(): id  0 | task 0 | main/do_checkpoint = no, pos_min = -1, pos_max = -1
+0.12.097.511 D set_adapters_lora: adapters = (nil)
+0.12.097.511 D adapters_lora_are_same: adapters = (nil)
+0.12.097.512 D set_embeddings: value = 0
+0.12.097.513 D srv        decode: n_batch (effective) = 128, off = 0
+0.12.127.949 D slot process_toke: id  0 | task 0 | stopped by limit, n_decoded = 1, n_predict = 1
+0.12.127.952 D slot process_toke: id  0 | task 0 | n_decoded = 1, n_remaining = 0, next token: 140066 '日表示'
+0.12.127.956 I slot print_timing: id  0 | task 0 | prompt eval time =      30.45 ms /     4 tokens (    7.61 ms per token,   131.38 tokens per second)
+0.12.127.958 I slot print_timing: id  0 | task 0 |        eval time =       0.00 ms /     1 tokens (    0.00 ms per token, 1000000.00 tokens per second)
+0.12.127.960 I slot print_timing: id  0 | task 0 |       total time =      30.45 ms /     5 tokens
+0.12.127.961 I slot print_timing: id  0 | task 0 |    graphs reused =          1
+0.12.127.968 D res          send: sending result for task id = 0
+0.12.127.968 D res          send: task id = 0 pushed to result queue
+0.12.127.971 I slot      release: id  0 | task 0 | stop processing: n_tokens = 4, truncated = 0
+0.12.127.972 D slot        reset: id  0 | task 0 | 
+0.12.127.976 D que    start_loop: waiting for new tasks
+0.12.127.977 D que    start_loop: processing new tasks
+0.12.127.980 D que    start_loop: processing task, id = 1
+0.12.127.981 D que    start_loop: update slots
+0.12.127.982 I srv  update_slots: all slots are idle
+0.12.127.982 D que    start_loop: waiting for new tasks
+0.12.127.990 D No parser definition detected, assuming pure content parser.0.12.128.012 D Parsed message: {"role":"assistant","content":"日表示"}
+0.12.128.105 D srv          stop: all tasks already finished, no need to cancel
+0.12.128.112 D res  remove_waiti: remove task 0 from waiting list. current waiting = 1 (before remove)
+0.12.128.112 D srv          stop: all tasks already finished, no need to cancel

MVP/evidence/teacher-quantize-q4_k_m.log

@@ -0,0 +1,369 @@
+ggml_vulkan: Found 1 Vulkan devices:
+ggml_vulkan: 0 = AMD Radeon RX 460 Graphics (RADV POLARIS11) (radv) | uma: 0 | fp16: 0 | bf16: 0 | fp4: 0 | warp size: 64 | shared memory: 65536 | int dot: 0 | matrix cores: none
+load_backend: loaded Vulkan backend from /usr/lib/ggml/libggml-vulkan.so
+load_backend: loaded CPU backend from /usr/lib/ggml/libggml-cpu-haswell.so
+llama_print_build_info: build = 10068 (571d0d540d)
+llama_print_build_info: built with GNU 16.1.1 for Linux x86_64
+llama_quantize: quantizing 'MVP/artifacts/qwen35-teacher-text-24-bf16.gguf' to 'MVP/artifacts/qwen35-teacher-text-24-q4_k_m.gguf' as Q4_K_M
+llama_model_loader: loaded meta data with 33 key-value pairs and 320 tensors from MVP/artifacts/qwen35-teacher-text-24-bf16.gguf (version GGUF V3 (latest))
+llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
+llama_model_loader: - kv   0:                       general.architecture str              = qwen35
+llama_model_loader: - kv   1:                               general.type str              = model
+llama_model_loader: - kv   2:                               general.name str              = Echo Qwen35 teacher.Fmw91y
+llama_model_loader: - kv   3:                         general.size_label str              = 752M
+llama_model_loader: - kv   4:                         qwen35.block_count u32              = 24
+llama_model_loader: - kv   5:                      qwen35.context_length u32              = 262144
+llama_model_loader: - kv   6:                    qwen35.embedding_length u32              = 1024
+llama_model_loader: - kv   7:                 qwen35.feed_forward_length u32              = 3584
+llama_model_loader: - kv   8:                qwen35.attention.head_count u32              = 8
+llama_model_loader: - kv   9:             qwen35.attention.head_count_kv u32              = 2
+llama_model_loader: - kv  10:             qwen35.rope.dimension_sections arr[i32,4]       = [11, 11, 10, 0]
+llama_model_loader: - kv  11:                      qwen35.rope.freq_base f32              = 10000000.000000
+llama_model_loader: - kv  12:    qwen35.attention.layer_norm_rms_epsilon f32              = 0.000001
+llama_model_loader: - kv  13:                qwen35.attention.key_length u32              = 256
+llama_model_loader: - kv  14:              qwen35.attention.value_length u32              = 256
+llama_model_loader: - kv  15:                          general.file_type u32              = 32
+llama_model_loader: - kv  16:                     qwen35.ssm.conv_kernel u32              = 4
+llama_model_loader: - kv  17:                      qwen35.ssm.state_size u32              = 128
+llama_model_loader: - kv  18:                     qwen35.ssm.group_count u32              = 16
+llama_model_loader: - kv  19:                  qwen35.ssm.time_step_rank u32              = 16
+llama_model_loader: - kv  20:                      qwen35.ssm.inner_size u32              = 2048
+llama_model_loader: - kv  21:             qwen35.full_attention_interval u32              = 4
+llama_model_loader: - kv  22:                qwen35.rope.dimension_count u32              = 64
+llama_model_loader: - kv  23:               general.quantization_version u32              = 2
+llama_model_loader: - kv  24:                       tokenizer.ggml.model str              = gpt2
+llama_model_loader: - kv  25:                         tokenizer.ggml.pre str              = qwen35
+llama_model_loader: - kv  26:                      tokenizer.ggml.tokens arr[str,248320]  = ["!", "\"", "#", "$", "%", "&", "'", ...
+llama_model_loader: - kv  27:                  tokenizer.ggml.token_type arr[i32,248320]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
+llama_model_loader: - kv  28:                      tokenizer.ggml.merges arr[str,247587]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
+llama_model_loader: - kv  29:                tokenizer.ggml.eos_token_id u32              = 248044
+llama_model_loader: - kv  30:            tokenizer.ggml.padding_token_id u32              = 248044
+llama_model_loader: - kv  31:               tokenizer.ggml.add_bos_token bool             = false
+llama_model_loader: - kv  32:                    tokenizer.chat_template str              = {%- set image_count = namespace(value...
+llama_model_loader: - type  f32:  133 tensors
+llama_model_loader: - type bf16:  187 tensors
+[   1/ 320] output_norm.weight                   - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[   2/ 320] token_embd.weight                    - [  1024, 248320,      1,      1], type =   bf16, converting to q6_K .. size =   485.00 MiB ->   198.93 MiB
+[   3/ 320] blk.0.attn_gate.weight               - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[   4/ 320] blk.0.attn_norm.weight               - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[   5/ 320] blk.0.attn_qkv.weight                - [  1024,   6144,      1,      1], type =   bf16, converting to q6_K .. size =    12.00 MiB ->     4.92 MiB
+[   6/ 320] blk.0.ffn_down.weight                - [  3584,   1024,      1,      1], type =   bf16, converting to q6_K .. size =     7.00 MiB ->     2.87 MiB
+[   7/ 320] blk.0.ffn_gate.weight                - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[   8/ 320] blk.0.ffn_up.weight                  - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[   9/ 320] blk.0.post_attention_norm.weight     - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  10/ 320] blk.0.ssm_a                          - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  11/ 320] blk.0.ssm_alpha.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  12/ 320] blk.0.ssm_beta.weight                - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  13/ 320] blk.0.ssm_conv1d.weight              - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[  14/ 320] blk.0.ssm_dt.bias                    - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  15/ 320] blk.0.ssm_norm.weight                - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  16/ 320] blk.0.ssm_out.weight                 - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  17/ 320] blk.1.attn_gate.weight               - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  18/ 320] blk.1.attn_norm.weight               - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  19/ 320] blk.1.attn_qkv.weight                - [  1024,   6144,      1,      1], type =   bf16, converting to q6_K .. size =    12.00 MiB ->     4.92 MiB
+[  20/ 320] blk.1.ffn_down.weight                - [  3584,   1024,      1,      1], type =   bf16, converting to q6_K .. size =     7.00 MiB ->     2.87 MiB
+[  21/ 320] blk.1.ffn_gate.weight                - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  22/ 320] blk.1.ffn_up.weight                  - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  23/ 320] blk.1.post_attention_norm.weight     - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  24/ 320] blk.1.ssm_a                          - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  25/ 320] blk.1.ssm_alpha.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  26/ 320] blk.1.ssm_beta.weight                - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  27/ 320] blk.1.ssm_conv1d.weight              - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[  28/ 320] blk.1.ssm_dt.bias                    - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  29/ 320] blk.1.ssm_norm.weight                - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  30/ 320] blk.1.ssm_out.weight                 - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  31/ 320] blk.2.attn_gate.weight               - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  32/ 320] blk.2.attn_norm.weight               - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  33/ 320] blk.2.attn_qkv.weight                - [  1024,   6144,      1,      1], type =   bf16, converting to q6_K .. size =    12.00 MiB ->     4.92 MiB
+[  34/ 320] blk.2.ffn_down.weight                - [  3584,   1024,      1,      1], type =   bf16, converting to q6_K .. size =     7.00 MiB ->     2.87 MiB
+[  35/ 320] blk.2.ffn_gate.weight                - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  36/ 320] blk.2.ffn_up.weight                  - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  37/ 320] blk.2.post_attention_norm.weight     - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  38/ 320] blk.2.ssm_a                          - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  39/ 320] blk.2.ssm_alpha.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  40/ 320] blk.2.ssm_beta.weight                - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  41/ 320] blk.2.ssm_conv1d.weight              - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[  42/ 320] blk.2.ssm_dt.bias                    - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  43/ 320] blk.2.ssm_norm.weight                - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  44/ 320] blk.2.ssm_out.weight                 - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  45/ 320] blk.3.attn_k.weight                  - [  1024,    512,      1,      1], type =   bf16, converting to q4_K .. size =     1.00 MiB ->     0.28 MiB
+[  46/ 320] blk.3.attn_k_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
+[  47/ 320] blk.3.attn_norm.weight               - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  48/ 320] blk.3.attn_output.weight             - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  49/ 320] blk.3.attn_q.weight                  - [  1024,   4096,      1,      1], type =   bf16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB
+[  50/ 320] blk.3.attn_q_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
+[  51/ 320] blk.3.attn_v.weight                  - [  1024,    512,      1,      1], type =   bf16, converting to q4_K .. size =     1.00 MiB ->     0.28 MiB
+[  52/ 320] blk.3.ffn_down.weight                - [  3584,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  53/ 320] blk.3.ffn_gate.weight                - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  54/ 320] blk.3.ffn_up.weight                  - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  55/ 320] blk.3.post_attention_norm.weight     - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  56/ 320] blk.4.attn_gate.weight               - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  57/ 320] blk.4.attn_norm.weight               - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  58/ 320] blk.4.attn_qkv.weight                - [  1024,   6144,      1,      1], type =   bf16, converting to q4_K .. size =    12.00 MiB ->     3.38 MiB
+[  59/ 320] blk.4.ffn_down.weight                - [  3584,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  60/ 320] blk.4.ffn_gate.weight                - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  61/ 320] blk.4.ffn_up.weight                  - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  62/ 320] blk.4.post_attention_norm.weight     - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  63/ 320] blk.4.ssm_a                          - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  64/ 320] blk.4.ssm_alpha.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  65/ 320] blk.4.ssm_beta.weight                - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  66/ 320] blk.4.ssm_conv1d.weight              - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[  67/ 320] blk.4.ssm_dt.bias                    - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  68/ 320] blk.4.ssm_norm.weight                - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  69/ 320] blk.4.ssm_out.weight                 - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  70/ 320] blk.5.attn_gate.weight               - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  71/ 320] blk.5.attn_norm.weight               - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  72/ 320] blk.5.attn_qkv.weight                - [  1024,   6144,      1,      1], type =   bf16, converting to q6_K .. size =    12.00 MiB ->     4.92 MiB
+[  73/ 320] blk.5.ffn_down.weight                - [  3584,   1024,      1,      1], type =   bf16, converting to q6_K .. size =     7.00 MiB ->     2.87 MiB
+[  74/ 320] blk.5.ffn_gate.weight                - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  75/ 320] blk.5.ffn_up.weight                  - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  76/ 320] blk.5.post_attention_norm.weight     - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  77/ 320] blk.5.ssm_a                          - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  78/ 320] blk.5.ssm_alpha.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  79/ 320] blk.5.ssm_beta.weight                - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  80/ 320] blk.5.ssm_conv1d.weight              - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[  81/ 320] blk.5.ssm_dt.bias                    - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  82/ 320] blk.5.ssm_norm.weight                - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  83/ 320] blk.5.ssm_out.weight                 - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  84/ 320] blk.6.attn_gate.weight               - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  85/ 320] blk.6.attn_norm.weight               - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  86/ 320] blk.6.attn_qkv.weight                - [  1024,   6144,      1,      1], type =   bf16, converting to q4_K .. size =    12.00 MiB ->     3.38 MiB
+[  87/ 320] blk.6.ffn_down.weight                - [  3584,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  88/ 320] blk.6.ffn_gate.weight                - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  89/ 320] blk.6.ffn_up.weight                  - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[  90/ 320] blk.6.post_attention_norm.weight     - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[  91/ 320] blk.6.ssm_a                          - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  92/ 320] blk.6.ssm_alpha.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  93/ 320] blk.6.ssm_beta.weight                - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[  94/ 320] blk.6.ssm_conv1d.weight              - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[  95/ 320] blk.6.ssm_dt.bias                    - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  96/ 320] blk.6.ssm_norm.weight                - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[  97/ 320] blk.6.ssm_out.weight                 - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[  98/ 320] blk.7.attn_k.weight                  - [  1024,    512,      1,      1], type =   bf16, converting to q4_K .. size =     1.00 MiB ->     0.28 MiB
+[  99/ 320] blk.7.attn_k_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
+[ 100/ 320] blk.7.attn_norm.weight               - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 101/ 320] blk.7.attn_output.weight             - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 102/ 320] blk.7.attn_q.weight                  - [  1024,   4096,      1,      1], type =   bf16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB
+[ 103/ 320] blk.7.attn_q_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
+[ 104/ 320] blk.7.attn_v.weight                  - [  1024,    512,      1,      1], type =   bf16, converting to q4_K .. size =     1.00 MiB ->     0.28 MiB
+[ 105/ 320] blk.7.ffn_down.weight                - [  3584,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 106/ 320] blk.7.ffn_gate.weight                - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 107/ 320] blk.7.ffn_up.weight                  - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 108/ 320] blk.7.post_attention_norm.weight     - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 109/ 320] blk.8.attn_gate.weight               - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 110/ 320] blk.8.attn_norm.weight               - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 111/ 320] blk.8.attn_qkv.weight                - [  1024,   6144,      1,      1], type =   bf16, converting to q6_K .. size =    12.00 MiB ->     4.92 MiB
+[ 112/ 320] blk.8.ffn_down.weight                - [  3584,   1024,      1,      1], type =   bf16, converting to q6_K .. size =     7.00 MiB ->     2.87 MiB
+[ 113/ 320] blk.8.ffn_gate.weight                - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 114/ 320] blk.8.ffn_up.weight                  - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 115/ 320] blk.8.post_attention_norm.weight     - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 116/ 320] blk.8.ssm_a                          - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 117/ 320] blk.8.ssm_alpha.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 118/ 320] blk.8.ssm_beta.weight                - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 119/ 320] blk.8.ssm_conv1d.weight              - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[ 120/ 320] blk.8.ssm_dt.bias                    - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 121/ 320] blk.8.ssm_norm.weight                - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 122/ 320] blk.8.ssm_out.weight                 - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 123/ 320] blk.9.attn_gate.weight               - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 124/ 320] blk.9.attn_norm.weight               - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 125/ 320] blk.9.attn_qkv.weight                - [  1024,   6144,      1,      1], type =   bf16, converting to q4_K .. size =    12.00 MiB ->     3.38 MiB
+[ 126/ 320] blk.9.ffn_down.weight                - [  3584,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 127/ 320] blk.9.ffn_gate.weight                - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 128/ 320] blk.9.ffn_up.weight                  - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 129/ 320] blk.9.post_attention_norm.weight     - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 130/ 320] blk.9.ssm_a                          - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 131/ 320] blk.9.ssm_alpha.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 132/ 320] blk.9.ssm_beta.weight                - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 133/ 320] blk.9.ssm_conv1d.weight              - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[ 134/ 320] blk.9.ssm_dt.bias                    - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 135/ 320] blk.9.ssm_norm.weight                - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 136/ 320] blk.9.ssm_out.weight                 - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 137/ 320] blk.10.attn_gate.weight              - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 138/ 320] blk.10.attn_norm.weight              - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 139/ 320] blk.10.attn_qkv.weight               - [  1024,   6144,      1,      1], type =   bf16, converting to q4_K .. size =    12.00 MiB ->     3.38 MiB
+[ 140/ 320] blk.10.ffn_down.weight               - [  3584,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 141/ 320] blk.10.ffn_gate.weight               - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 142/ 320] blk.10.ffn_up.weight                 - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 143/ 320] blk.10.post_attention_norm.weight    - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 144/ 320] blk.10.ssm_a                         - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 145/ 320] blk.10.ssm_alpha.weight              - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 146/ 320] blk.10.ssm_beta.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 147/ 320] blk.10.ssm_conv1d.weight             - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[ 148/ 320] blk.10.ssm_dt.bias                   - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 149/ 320] blk.10.ssm_norm.weight               - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 150/ 320] blk.10.ssm_out.weight                - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 151/ 320] blk.11.attn_k.weight                 - [  1024,    512,      1,      1], type =   bf16, converting to q4_K .. size =     1.00 MiB ->     0.28 MiB
+[ 152/ 320] blk.11.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
+[ 153/ 320] blk.11.attn_norm.weight              - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 154/ 320] blk.11.attn_output.weight            - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 155/ 320] blk.11.attn_q.weight                 - [  1024,   4096,      1,      1], type =   bf16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB
+[ 156/ 320] blk.11.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
+[ 157/ 320] blk.11.attn_v.weight                 - [  1024,    512,      1,      1], type =   bf16, converting to q6_K .. size =     1.00 MiB ->     0.41 MiB
+[ 158/ 320] blk.11.ffn_down.weight               - [  3584,   1024,      1,      1], type =   bf16, converting to q6_K .. size =     7.00 MiB ->     2.87 MiB
+[ 159/ 320] blk.11.ffn_gate.weight               - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 160/ 320] blk.11.ffn_up.weight                 - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 161/ 320] blk.11.post_attention_norm.weight    - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 162/ 320] blk.12.attn_gate.weight              - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 163/ 320] blk.12.attn_norm.weight              - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 164/ 320] blk.12.attn_qkv.weight               - [  1024,   6144,      1,      1], type =   bf16, converting to q4_K .. size =    12.00 MiB ->     3.38 MiB
+[ 165/ 320] blk.12.ffn_down.weight               - [  3584,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 166/ 320] blk.12.ffn_gate.weight               - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 167/ 320] blk.12.ffn_up.weight                 - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 168/ 320] blk.12.post_attention_norm.weight    - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 169/ 320] blk.12.ssm_a                         - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 170/ 320] blk.12.ssm_alpha.weight              - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 171/ 320] blk.12.ssm_beta.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 172/ 320] blk.12.ssm_conv1d.weight             - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[ 173/ 320] blk.12.ssm_dt.bias                   - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 174/ 320] blk.12.ssm_norm.weight               - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 175/ 320] blk.12.ssm_out.weight                - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 176/ 320] blk.13.attn_gate.weight              - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 177/ 320] blk.13.attn_norm.weight              - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 178/ 320] blk.13.attn_qkv.weight               - [  1024,   6144,      1,      1], type =   bf16, converting to q4_K .. size =    12.00 MiB ->     3.38 MiB
+[ 179/ 320] blk.13.ffn_down.weight               - [  3584,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 180/ 320] blk.13.ffn_gate.weight               - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 181/ 320] blk.13.ffn_up.weight                 - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 182/ 320] blk.13.post_attention_norm.weight    - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 183/ 320] blk.13.ssm_a                         - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 184/ 320] blk.13.ssm_alpha.weight              - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 185/ 320] blk.13.ssm_beta.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 186/ 320] blk.13.ssm_conv1d.weight             - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[ 187/ 320] blk.13.ssm_dt.bias                   - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 188/ 320] blk.13.ssm_norm.weight               - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 189/ 320] blk.13.ssm_out.weight                - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 190/ 320] blk.14.attn_gate.weight              - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 191/ 320] blk.14.attn_norm.weight              - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 192/ 320] blk.14.attn_qkv.weight               - [  1024,   6144,      1,      1], type =   bf16, converting to q6_K .. size =    12.00 MiB ->     4.92 MiB
+[ 193/ 320] blk.14.ffn_down.weight               - [  3584,   1024,      1,      1], type =   bf16, converting to q6_K .. size =     7.00 MiB ->     2.87 MiB
+[ 194/ 320] blk.14.ffn_gate.weight               - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 195/ 320] blk.14.ffn_up.weight                 - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 196/ 320] blk.14.post_attention_norm.weight    - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 197/ 320] blk.14.ssm_a                         - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 198/ 320] blk.14.ssm_alpha.weight              - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 199/ 320] blk.14.ssm_beta.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 200/ 320] blk.14.ssm_conv1d.weight             - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[ 201/ 320] blk.14.ssm_dt.bias                   - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 202/ 320] blk.14.ssm_norm.weight               - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 203/ 320] blk.14.ssm_out.weight                - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 204/ 320] blk.15.attn_k.weight                 - [  1024,    512,      1,      1], type =   bf16, converting to q4_K .. size =     1.00 MiB ->     0.28 MiB
+[ 205/ 320] blk.15.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
+[ 206/ 320] blk.15.attn_norm.weight              - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 207/ 320] blk.15.attn_output.weight            - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 208/ 320] blk.15.attn_q.weight                 - [  1024,   4096,      1,      1], type =   bf16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB
+[ 209/ 320] blk.15.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
+[ 210/ 320] blk.15.attn_v.weight                 - [  1024,    512,      1,      1], type =   bf16, converting to q4_K .. size =     1.00 MiB ->     0.28 MiB
+[ 211/ 320] blk.15.ffn_down.weight               - [  3584,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 212/ 320] blk.15.ffn_gate.weight               - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 213/ 320] blk.15.ffn_up.weight                 - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 214/ 320] blk.15.post_attention_norm.weight    - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 215/ 320] blk.16.attn_gate.weight              - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 216/ 320] blk.16.attn_norm.weight              - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 217/ 320] blk.16.attn_qkv.weight               - [  1024,   6144,      1,      1], type =   bf16, converting to q4_K .. size =    12.00 MiB ->     3.38 MiB
+[ 218/ 320] blk.16.ffn_down.weight               - [  3584,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 219/ 320] blk.16.ffn_gate.weight               - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 220/ 320] blk.16.ffn_up.weight                 - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 221/ 320] blk.16.post_attention_norm.weight    - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 222/ 320] blk.16.ssm_a                         - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 223/ 320] blk.16.ssm_alpha.weight              - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 224/ 320] blk.16.ssm_beta.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 225/ 320] blk.16.ssm_conv1d.weight             - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[ 226/ 320] blk.16.ssm_dt.bias                   - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 227/ 320] blk.16.ssm_norm.weight               - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 228/ 320] blk.16.ssm_out.weight                - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 229/ 320] blk.17.attn_gate.weight              - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 230/ 320] blk.17.attn_norm.weight              - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 231/ 320] blk.17.attn_qkv.weight               - [  1024,   6144,      1,      1], type =   bf16, converting to q6_K .. size =    12.00 MiB ->     4.92 MiB
+[ 232/ 320] blk.17.ffn_down.weight               - [  3584,   1024,      1,      1], type =   bf16, converting to q6_K .. size =     7.00 MiB ->     2.87 MiB
+[ 233/ 320] blk.17.ffn_gate.weight               - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 234/ 320] blk.17.ffn_up.weight                 - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 235/ 320] blk.17.post_attention_norm.weight    - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 236/ 320] blk.17.ssm_a                         - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 237/ 320] blk.17.ssm_alpha.weight              - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 238/ 320] blk.17.ssm_beta.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 239/ 320] blk.17.ssm_conv1d.weight             - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[ 240/ 320] blk.17.ssm_dt.bias                   - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 241/ 320] blk.17.ssm_norm.weight               - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 242/ 320] blk.17.ssm_out.weight                - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 243/ 320] blk.18.attn_gate.weight              - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 244/ 320] blk.18.attn_norm.weight              - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 245/ 320] blk.18.attn_qkv.weight               - [  1024,   6144,      1,      1], type =   bf16, converting to q4_K .. size =    12.00 MiB ->     3.38 MiB
+[ 246/ 320] blk.18.ffn_down.weight               - [  3584,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 247/ 320] blk.18.ffn_gate.weight               - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 248/ 320] blk.18.ffn_up.weight                 - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 249/ 320] blk.18.post_attention_norm.weight    - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 250/ 320] blk.18.ssm_a                         - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 251/ 320] blk.18.ssm_alpha.weight              - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 252/ 320] blk.18.ssm_beta.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 253/ 320] blk.18.ssm_conv1d.weight             - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[ 254/ 320] blk.18.ssm_dt.bias                   - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 255/ 320] blk.18.ssm_norm.weight               - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 256/ 320] blk.18.ssm_out.weight                - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 257/ 320] blk.19.attn_k.weight                 - [  1024,    512,      1,      1], type =   bf16, converting to q4_K .. size =     1.00 MiB ->     0.28 MiB
+[ 258/ 320] blk.19.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
+[ 259/ 320] blk.19.attn_norm.weight              - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 260/ 320] blk.19.attn_output.weight            - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 261/ 320] blk.19.attn_q.weight                 - [  1024,   4096,      1,      1], type =   bf16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB
+[ 262/ 320] blk.19.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
+[ 263/ 320] blk.19.attn_v.weight                 - [  1024,    512,      1,      1], type =   bf16, converting to q4_K .. size =     1.00 MiB ->     0.28 MiB
+[ 264/ 320] blk.19.ffn_down.weight               - [  3584,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 265/ 320] blk.19.ffn_gate.weight               - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 266/ 320] blk.19.ffn_up.weight                 - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 267/ 320] blk.19.post_attention_norm.weight    - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 268/ 320] blk.20.attn_gate.weight              - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 269/ 320] blk.20.attn_norm.weight              - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 270/ 320] blk.20.attn_qkv.weight               - [  1024,   6144,      1,      1], type =   bf16, converting to q6_K .. size =    12.00 MiB ->     4.92 MiB
+[ 271/ 320] blk.20.ffn_down.weight               - [  3584,   1024,      1,      1], type =   bf16, converting to q6_K .. size =     7.00 MiB ->     2.87 MiB
+[ 272/ 320] blk.20.ffn_gate.weight               - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 273/ 320] blk.20.ffn_up.weight                 - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 274/ 320] blk.20.post_attention_norm.weight    - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 275/ 320] blk.20.ssm_a                         - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 276/ 320] blk.20.ssm_alpha.weight              - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 277/ 320] blk.20.ssm_beta.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 278/ 320] blk.20.ssm_conv1d.weight             - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[ 279/ 320] blk.20.ssm_dt.bias                   - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 280/ 320] blk.20.ssm_norm.weight               - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 281/ 320] blk.20.ssm_out.weight                - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 282/ 320] blk.21.attn_gate.weight              - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 283/ 320] blk.21.attn_norm.weight              - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 284/ 320] blk.21.attn_qkv.weight               - [  1024,   6144,      1,      1], type =   bf16, converting to q6_K .. size =    12.00 MiB ->     4.92 MiB
+[ 285/ 320] blk.21.ffn_down.weight               - [  3584,   1024,      1,      1], type =   bf16, converting to q6_K .. size =     7.00 MiB ->     2.87 MiB
+[ 286/ 320] blk.21.ffn_gate.weight               - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 287/ 320] blk.21.ffn_up.weight                 - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 288/ 320] blk.21.post_attention_norm.weight    - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 289/ 320] blk.21.ssm_a                         - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 290/ 320] blk.21.ssm_alpha.weight              - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 291/ 320] blk.21.ssm_beta.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 292/ 320] blk.21.ssm_conv1d.weight             - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[ 293/ 320] blk.21.ssm_dt.bias                   - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 294/ 320] blk.21.ssm_norm.weight               - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 295/ 320] blk.21.ssm_out.weight                - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 296/ 320] blk.22.attn_gate.weight              - [  1024,   2048,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 297/ 320] blk.22.attn_norm.weight              - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 298/ 320] blk.22.attn_qkv.weight               - [  1024,   6144,      1,      1], type =   bf16, converting to q6_K .. size =    12.00 MiB ->     4.92 MiB
+[ 299/ 320] blk.22.ffn_down.weight               - [  3584,   1024,      1,      1], type =   bf16, converting to q6_K .. size =     7.00 MiB ->     2.87 MiB
+[ 300/ 320] blk.22.ffn_gate.weight               - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 301/ 320] blk.22.ffn_up.weight                 - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 302/ 320] blk.22.post_attention_norm.weight    - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 303/ 320] blk.22.ssm_a                         - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 304/ 320] blk.22.ssm_alpha.weight              - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 305/ 320] blk.22.ssm_beta.weight               - [  1024,     16,      1,      1], type =   bf16, converting to q4_K .. size =     0.03 MiB ->     0.01 MiB
+[ 306/ 320] blk.22.ssm_conv1d.weight             - [     4,   6144,      1,      1], type =    f32, size =    0.094 MiB
+[ 307/ 320] blk.22.ssm_dt.bias                   - [    16,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 308/ 320] blk.22.ssm_norm.weight               - [   128,      1,      1,      1], type =    f32, size =    0.000 MiB
+[ 309/ 320] blk.22.ssm_out.weight                - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 310/ 320] blk.23.attn_k.weight                 - [  1024,    512,      1,      1], type =   bf16, converting to q4_K .. size =     1.00 MiB ->     0.28 MiB
+[ 311/ 320] blk.23.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
+[ 312/ 320] blk.23.attn_norm.weight              - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+[ 313/ 320] blk.23.attn_output.weight            - [  2048,   1024,      1,      1], type =   bf16, converting to q4_K .. size =     4.00 MiB ->     1.12 MiB
+[ 314/ 320] blk.23.attn_q.weight                 - [  1024,   4096,      1,      1], type =   bf16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB
+[ 315/ 320] blk.23.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
+[ 316/ 320] blk.23.attn_v.weight                 - [  1024,    512,      1,      1], type =   bf16, converting to q6_K .. size =     1.00 MiB ->     0.41 MiB
+[ 317/ 320] blk.23.ffn_down.weight               - [  3584,   1024,      1,      1], type =   bf16, converting to q6_K .. size =     7.00 MiB ->     2.87 MiB
+[ 318/ 320] blk.23.ffn_gate.weight               - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 319/ 320] blk.23.ffn_up.weight                 - [  1024,   3584,      1,      1], type =   bf16, converting to q4_K .. size =     7.00 MiB ->     1.97 MiB
+[ 320/ 320] blk.23.post_attention_norm.weight    - [  1024,      1,      1,      1], type =    f32, size =    0.004 MiB
+llama_model_quantize_impl: model size  =  1436.03 MiB (16.01 BPW)
+llama_model_quantize_impl: quant size  =   494.32 MiB (5.51 BPW)
+
+llama_quantize: quantize time =  8743.03 ms
+llama_quantize:    total time =  8743.03 ms

MVP/evidence/teacher-vulkan-no-host.log

@@ -0,0 +1,1231 @@
+0.00.160.437 I cmn  common_param: common_params_print_info: build 10068 (571d0d540d) with GNU 16.1.1 for Linux x86_64 (debug)
+0.00.160.440 I cmn  common_param: common_params_print_info: verbosity = 2147483647 (adjust with the `-lv N` CLI arg)
+0.00.160.441 I cmn  common_param: device_info:
+0.00.160.522 I cmn  common_param:   - Vulkan0 : AMD Radeon RX 460 Graphics (RADV POLARIS11) (2048 MiB, 1453 MiB free)
+0.00.160.529 I cmn  common_param:   - CPU     : AMD Ryzen 5 3600 6-Core Processor (15914 MiB, 15914 MiB free)
+0.00.160.545 I cmn  common_param: system_info: n_threads = 6 (n_threads_batch = 6) / 12 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | OPENMP = 1 | REPACK = 1 | 
+0.00.160.572 I srv          init: running without SSL
+0.00.160.629 I srv          init: using 11 threads for HTTP server
+0.00.160.631 I srv          init: The UI is disabled
+0.00.160.631 I srv          init: Use --ui/--no-ui (or deprecated --webui/--no-webui) to enable/disable
+0.00.160.722 W srv  llama_server: -----------------
+0.00.160.723 W srv  llama_server: CORS is set to allow all origins ('*') and no API key is set
+0.00.160.724 W srv  llama_server: this can be a security risk (cross-origin attacks)
+0.00.160.724 W srv  llama_server: more info: https://github.com/ggml-org/llama.cpp/pull/25655
+0.00.160.724 W srv  llama_server: -----------------
+0.00.160.742 I srv         start: binding port with default address family
+0.00.161.969 I srv    load_model: loading model 'MVP/artifacts/qwen35-teacher-text-24-bf16.gguf'
+0.00.161.971 I srv    load_model: local path 'MVP/artifacts/qwen35-teacher-text-24-bf16.gguf'
+0.00.161.981 I cmn  common_init_: fitting params to device memory ...
+0.00.161.982 I cmn  common_init_: (for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on)
+0.00.161.982 I common_params_fit_impl: getting device memory data for initial parameters:
+0.00.210.544 I llama_model_loader: loaded meta data with 33 key-value pairs and 320 tensors from MVP/artifacts/qwen35-teacher-text-24-bf16.gguf (version GGUF V3 (latest))
+0.00.210.558 I llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
+0.00.210.561 I llama_model_loader: - kv   0:                       general.architecture str              = qwen35
+0.00.210.562 I llama_model_loader: - kv   1:                               general.type str              = model
+0.00.210.563 I llama_model_loader: - kv   2:                               general.name str              = Echo Qwen35 teacher.Fmw91y
+0.00.210.563 I llama_model_loader: - kv   3:                         general.size_label str              = 752M
+0.00.210.564 I llama_model_loader: - kv   4:                         qwen35.block_count u32              = 24
+0.00.210.565 I llama_model_loader: - kv   5:                      qwen35.context_length u32              = 262144
+0.00.210.565 I llama_model_loader: - kv   6:                    qwen35.embedding_length u32              = 1024
+0.00.210.566 I llama_model_loader: - kv   7:                 qwen35.feed_forward_length u32              = 3584
+0.00.210.566 I llama_model_loader: - kv   8:                qwen35.attention.head_count u32              = 8
+0.00.210.566 I llama_model_loader: - kv   9:             qwen35.attention.head_count_kv u32              = 2
+0.00.210.573 I llama_model_loader: - kv  10:             qwen35.rope.dimension_sections arr[i32,4]       = [11, 11, 10, 0]
+0.00.210.578 I llama_model_loader: - kv  11:                      qwen35.rope.freq_base f32              = 10000000.000000
+0.00.210.579 I llama_model_loader: - kv  12:    qwen35.attention.layer_norm_rms_epsilon f32              = 0.000001
+0.00.210.579 I llama_model_loader: - kv  13:                qwen35.attention.key_length u32              = 256
+0.00.210.580 I llama_model_loader: - kv  14:              qwen35.attention.value_length u32              = 256
+0.00.210.580 I llama_model_loader: - kv  15:                          general.file_type u32              = 32
+0.00.210.580 I llama_model_loader: - kv  16:                     qwen35.ssm.conv_kernel u32              = 4
+0.00.210.581 I llama_model_loader: - kv  17:                      qwen35.ssm.state_size u32              = 128
+0.00.210.581 I llama_model_loader: - kv  18:                     qwen35.ssm.group_count u32              = 16
+0.00.210.582 I llama_model_loader: - kv  19:                  qwen35.ssm.time_step_rank u32              = 16
+0.00.210.582 I llama_model_loader: - kv  20:                      qwen35.ssm.inner_size u32              = 2048
+0.00.210.582 I llama_model_loader: - kv  21:             qwen35.full_attention_interval u32              = 4
+0.00.210.583 I llama_model_loader: - kv  22:                qwen35.rope.dimension_count u32              = 64
+0.00.210.583 I llama_model_loader: - kv  23:               general.quantization_version u32              = 2
+0.00.210.584 I llama_model_loader: - kv  24:                       tokenizer.ggml.model str              = gpt2
+0.00.210.584 I llama_model_loader: - kv  25:                         tokenizer.ggml.pre str              = qwen35
+0.00.236.050 I llama_model_loader: - kv  26:                      tokenizer.ggml.tokens arr[str,248320]  = ["!", "\"", "#", "$", "%", "&", "'", ...
+0.00.242.630 I llama_model_loader: - kv  27:                  tokenizer.ggml.token_type arr[i32,248320]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
+0.00.266.757 I llama_model_loader: - kv  28:                      tokenizer.ggml.merges arr[str,247587]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
+0.00.266.765 I llama_model_loader: - kv  29:                tokenizer.ggml.eos_token_id u32              = 248044
+0.00.266.765 I llama_model_loader: - kv  30:            tokenizer.ggml.padding_token_id u32              = 248044
+0.00.266.766 I llama_model_loader: - kv  31:               tokenizer.ggml.add_bos_token bool             = false
+0.00.266.771 I llama_model_loader: - kv  32:                    tokenizer.chat_template str              = {%- set image_count = namespace(value...
+0.00.266.772 I llama_model_loader: - type  f32:  133 tensors
+0.00.266.773 I llama_model_loader: - type bf16:  187 tensors
+0.00.266.774 I print_info: file format = GGUF V3 (latest)
+0.00.266.775 I print_info: file type   = BF16
+0.00.266.781 I print_info: file size   = 1.40 GiB (16.01 BPW) 
+0.00.266.933 I llama_prepare_model_devices: using device Vulkan0 (AMD Radeon RX 460 Graphics (RADV POLARIS11)) (0000:29:00.0) - 1453 MiB free
+0.00.424.434 D init_tokenizer: initializing tokenizer for type 2
+0.00.460.773 I load: 0 unused tokens
+0.00.460.788 D load: control token: 248075 '<tts_text_bos_single>' is not marked as EOG
+0.00.460.789 D load: control token: 248073 '<tts_text_bos>' is not marked as EOG
+0.00.460.789 D load: control token: 248072 '<tts_pad>' is not marked as EOG
+0.00.460.790 D load: control token: 248071 '<|audio_end|>' is not marked as EOG
+0.00.460.791 D load: control token: 248061 '<|fim_middle|>' is not marked as EOG
+0.00.460.791 D load: control token: 248055 '<|vision_pad|>' is not marked as EOG
+0.00.460.792 D load: control token: 248052 '<|quad_end|>' is not marked as EOG
+0.00.460.792 D load: control token: 248049 '<|box_start|>' is not marked as EOG
+0.00.460.792 D load: control token: 248048 '<|object_ref_end|>' is not marked as EOG
+0.00.460.794 D load: control token: 248045 '<|im_start|>' is not marked as EOG
+0.00.461.140 D load: control token: 248057 '<|video_pad|>' is not marked as EOG
+0.00.463.012 D load: control token: 248070 '<|audio_start|>' is not marked as EOG
+0.00.463.117 D load: control token: 248056 '<|image_pad|>' is not marked as EOG
+0.00.463.330 D load: control token: 248054 '<|vision_end|>' is not marked as EOG
+0.00.466.852 D load: control token: 248060 '<|fim_prefix|>' is not marked as EOG
+0.00.467.364 D load: control token: 248050 '<|box_end|>' is not marked as EOG
+0.00.474.555 D load: control token: 248074 '<tts_text_eod>' is not marked as EOG
+0.00.483.739 D load: control token: 248053 '<|vision_start|>' is not marked as EOG
+0.00.486.201 D load: control token: 248062 '<|fim_suffix|>' is not marked as EOG
+0.00.488.168 D load: control token: 248047 '<|object_ref_start|>' is not marked as EOG
+0.00.489.713 D load: control token: 248051 '<|quad_start|>' is not marked as EOG
+0.00.491.567 D load: control token: 248076 '<|audio_pad|>' is not marked as EOG
+0.00.511.957 I load: printing all EOG tokens:
+0.00.511.963 I load:   - 248044 ('<|endoftext|>')
+0.00.511.963 I load:   - 248046 ('<|im_end|>')
+0.00.511.964 I load:   - 248063 ('<|fim_pad|>')
+0.00.511.964 I load:   - 248064 ('<|repo_name|>')
+0.00.511.964 I load:   - 248065 ('<|file_sep|>')
+0.00.512.744 I load: special tokens cache size = 33
+0.00.603.047 I load: token to piece cache size = 1.7581 MB
+0.00.603.060 I print_info: arch                  = qwen35
+0.00.603.061 I print_info: vocab_only            = 0
+0.00.603.062 I print_info: no_alloc              = 1
+0.00.603.062 I print_info: n_ctx_train           = 262144
+0.00.603.063 I print_info: n_embd_inp            = 1024
+0.00.603.063 I print_info: n_embd                = 1024
+0.00.603.064 I print_info: n_embd_out            = 1024
+0.00.603.065 I print_info: n_layer               = 24
+0.00.603.065 I print_info: n_layer_all           = 24
+0.00.603.073 I print_info: n_head                = 8
+0.00.603.074 I print_info: n_head_kv             = 2
+0.00.603.074 I print_info: n_rot                 = 64
+0.00.603.075 I print_info: n_swa                 = 0
+0.00.603.075 I print_info: is_swa_any            = 0
+0.00.603.075 I print_info: n_embd_head_k         = 256
+0.00.603.076 I print_info: n_embd_head_v         = 256
+0.00.603.076 I print_info: n_gqa                 = 4
+0.00.603.078 I print_info: n_embd_k_gqa          = 512
+0.00.603.078 I print_info: n_embd_v_gqa          = 512
+0.00.603.079 I print_info: f_norm_eps            = 0.0e+00
+0.00.603.080 I print_info: f_norm_rms_eps        = 1.0e-06
+0.00.603.081 I print_info: f_clamp_kqv           = 0.0e+00
+0.00.603.081 I print_info: f_max_alibi_bias      = 0.0e+00
+0.00.603.081 I print_info: f_logit_scale         = 0.0e+00
+0.00.603.082 I print_info: f_attn_scale          = 0.0e+00
+0.00.603.082 I print_info: f_attn_value_scale    = 0.0000
+0.00.603.083 I print_info: n_ff                  = 3584
+0.00.603.083 I print_info: n_expert              = 0
+0.00.603.083 I print_info: n_expert_used         = 0
+0.00.603.083 I print_info: n_expert_groups       = 0
+0.00.603.084 I print_info: n_group_used          = 0
+0.00.603.084 I print_info: causal attn           = 1
+0.00.603.084 I print_info: pooling type          = -1
+0.00.603.084 I print_info: rope type             = 40
+0.00.603.084 I print_info: rope scaling          = linear
+0.00.603.086 I print_info: freq_base_train       = 10000000.0
+0.00.603.087 I print_info: freq_scale_train      = 1
+0.00.603.087 I print_info: n_ctx_orig_yarn       = 262144
+0.00.603.088 I print_info: rope_yarn_log_mul     = 0.0000
+0.00.603.088 I print_info: rope_finetuned        = unknown
+0.00.603.088 I print_info: mrope sections        = [11, 11, 10, 0]
+0.00.603.089 I print_info: ssm_d_conv            = 4
+0.00.603.089 I print_info: ssm_d_inner           = 2048
+0.00.603.089 I print_info: ssm_d_state           = 128
+0.00.603.089 I print_info: ssm_dt_rank           = 16
+0.00.603.090 I print_info: ssm_n_group           = 16
+0.00.603.090 I print_info: ssm_dt_b_c_rms        = 0
+0.00.603.090 I print_info: model type            = 0.8B
+0.00.603.091 I print_info: model params          = 752.39 M
+0.00.603.091 I print_info: general.name          = Echo Qwen35 teacher.Fmw91y
+0.00.603.092 I print_info: vocab type            = BPE
+0.00.603.093 I print_info: n_vocab               = 248320
+0.00.603.093 I print_info: n_merges              = 247587
+0.00.603.094 I print_info: BOS token             = 11 ','
+0.00.603.094 I print_info: EOS token             = 248044 '<|endoftext|>'
+0.00.603.094 I print_info: EOT token             = 248046 '<|im_end|>'
+0.00.603.095 I print_info: PAD token             = 248044 '<|endoftext|>'
+0.00.603.095 I print_info: LF token              = 198 'Ċ'
+0.00.603.095 I print_info: FIM PRE token         = 248060 '<|fim_prefix|>'
+0.00.603.096 I print_info: FIM SUF token         = 248062 '<|fim_suffix|>'
+0.00.603.096 I print_info: FIM MID token         = 248061 '<|fim_middle|>'
+0.00.603.096 I print_info: FIM PAD token         = 248063 '<|fim_pad|>'
+0.00.603.096 I print_info: FIM REP token         = 248064 '<|repo_name|>'
+0.00.603.097 I print_info: FIM SEP token         = 248065 '<|file_sep|>'
+0.00.603.097 I print_info: EOG token             = 248044 '<|endoftext|>'
+0.00.603.098 I print_info: EOG token             = 248046 '<|im_end|>'
+0.00.603.098 I print_info: EOG token             = 248063 '<|fim_pad|>'
+0.00.603.098 I print_info: EOG token             = 248064 '<|repo_name|>'
+0.00.603.099 I print_info: EOG token             = 248065 '<|file_sep|>'
+0.00.603.099 I print_info: max token length      = 256
+0.00.603.100 I load_tensors: loading model tensors, this can take a while... (mmap = false, direct_io = false)
+0.00.613.035 D load_tensors: layer   0 assigned to device Vulkan0, is_swa = 0
+0.00.613.037 D load_tensors: layer   1 assigned to device Vulkan0, is_swa = 0
+0.00.613.038 D load_tensors: layer   2 assigned to device Vulkan0, is_swa = 0
+0.00.613.038 D load_tensors: layer   3 assigned to device Vulkan0, is_swa = 0
+0.00.613.038 D load_tensors: layer   4 assigned to device Vulkan0, is_swa = 0
+0.00.613.039 D load_tensors: layer   5 assigned to device Vulkan0, is_swa = 0
+0.00.613.039 D load_tensors: layer   6 assigned to device Vulkan0, is_swa = 0
+0.00.613.039 D load_tensors: layer   7 assigned to device Vulkan0, is_swa = 0
+0.00.613.039 D load_tensors: layer   8 assigned to device Vulkan0, is_swa = 0
+0.00.613.039 D load_tensors: layer   9 assigned to device Vulkan0, is_swa = 0
+0.00.613.040 D load_tensors: layer  10 assigned to device Vulkan0, is_swa = 0
+0.00.613.040 D load_tensors: layer  11 assigned to device Vulkan0, is_swa = 0
+0.00.613.041 D load_tensors: layer  12 assigned to device Vulkan0, is_swa = 0
+0.00.613.041 D load_tensors: layer  13 assigned to device Vulkan0, is_swa = 0
+0.00.613.041 D load_tensors: layer  14 assigned to device Vulkan0, is_swa = 0
+0.00.613.042 D load_tensors: layer  15 assigned to device Vulkan0, is_swa = 0
+0.00.613.042 D load_tensors: layer  16 assigned to device Vulkan0, is_swa = 0
+0.00.613.042 D load_tensors: layer  17 assigned to device Vulkan0, is_swa = 0
+0.00.613.043 D load_tensors: layer  18 assigned to device Vulkan0, is_swa = 0
+0.00.613.043 D load_tensors: layer  19 assigned to device Vulkan0, is_swa = 0
+0.00.613.043 D load_tensors: layer  20 assigned to device Vulkan0, is_swa = 0
+0.00.613.044 D load_tensors: layer  21 assigned to device Vulkan0, is_swa = 0
+0.00.613.044 D load_tensors: layer  22 assigned to device Vulkan0, is_swa = 0
+0.00.613.044 D load_tensors: layer  23 assigned to device Vulkan0, is_swa = 0
+0.00.613.045 D load_tensors: layer  24 assigned to device Vulkan0, is_swa = 0
+0.00.613.083 D create_tensor: loading tensor token_embd.weight
+0.00.613.108 D create_tensor: loading tensor output_norm.weight
+0.00.613.117 D create_tensor: loading tensor token_embd.weight
+0.00.613.122 D create_tensor: loading tensor blk.0.attn_norm.weight
+0.00.613.129 D create_tensor: loading tensor blk.0.post_attention_norm.weight
+0.00.613.134 D create_tensor: loading tensor blk.0.attn_qkv.weight
+0.00.613.140 D create_tensor: loading tensor blk.0.attn_gate.weight
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+0.00.614.803 D create_tensor: loading tensor blk.22.attn_norm.weight
+0.00.614.811 D create_tensor: loading tensor blk.22.post_attention_norm.weight
+0.00.614.817 D create_tensor: loading tensor blk.22.attn_qkv.weight
+0.00.614.823 D create_tensor: loading tensor blk.22.attn_gate.weight
+0.00.614.829 D create_tensor: loading tensor blk.22.ssm_conv1d.weight
+0.00.614.834 D create_tensor: loading tensor blk.22.ssm_dt.bias
+0.00.614.840 D create_tensor: loading tensor blk.22.ssm_a
+0.00.614.846 D create_tensor: loading tensor blk.22.ssm_beta.weight
+0.00.614.852 D create_tensor: loading tensor blk.22.ssm_alpha.weight
+0.00.614.857 D create_tensor: loading tensor blk.22.ssm_norm.weight
+0.00.614.863 D create_tensor: loading tensor blk.22.ssm_out.weight
+0.00.614.869 D create_tensor: loading tensor blk.22.ffn_gate.weight
+0.00.614.877 D create_tensor: loading tensor blk.22.ffn_down.weight
+0.00.614.882 D create_tensor: loading tensor blk.22.ffn_up.weight
+0.00.614.888 D create_tensor: loading tensor blk.23.attn_norm.weight
+0.00.614.894 D create_tensor: loading tensor blk.23.post_attention_norm.weight
+0.00.614.912 D create_tensor: loading tensor blk.23.attn_q.weight
+0.00.614.920 D create_tensor: loading tensor blk.23.attn_k.weight
+0.00.614.927 D create_tensor: loading tensor blk.23.attn_v.weight
+0.00.614.941 D create_tensor: loading tensor blk.23.attn_output.weight
+0.00.614.947 D create_tensor: loading tensor blk.23.attn_q_norm.weight
+0.00.614.953 D create_tensor: loading tensor blk.23.attn_k_norm.weight
+0.00.614.959 D create_tensor: loading tensor blk.23.ffn_gate.weight
+0.00.614.967 D create_tensor: loading tensor blk.23.ffn_down.weight
+0.00.614.973 D create_tensor: loading tensor blk.23.ffn_up.weight
+0.00.615.824 D done_getting_tensors: tensor 'token_embd.weight' (bf16) (and 0 others) cannot be used with preferred buffer type CPU_REPACK, using CPU instead
+0.00.615.951 I load_tensors: offloading output layer to GPU
+0.00.615.951 I load_tensors: offloading 23 repeating layers to GPU
+0.00.615.952 I load_tensors: offloaded 25/25 layers to GPU
+0.00.615.955 I load_tensors:          CPU model buffer size =     0.00 MiB
+0.00.615.955 I load_tensors:      Vulkan0 model buffer size =     0.00 MiB
+0.00.618.919 I llama_context: constructing llama_context
+0.00.618.923 I llama_context: n_seq_max     = 1
+0.00.618.923 I llama_context: n_ctx         = 256
+0.00.618.923 I llama_context: n_ctx_seq     = 256
+0.00.618.924 I llama_context: n_batch       = 128
+0.00.618.924 I llama_context: n_ubatch      = 1
+0.00.618.924 I llama_context: causal_attn   = 1
+0.00.618.925 I llama_context: flash_attn    = auto
+0.00.618.925 I llama_context: kv_unified    = false
+0.00.618.930 I llama_context: freq_base     = 10000000.0
+0.00.618.931 I llama_context: freq_scale    = 1
+0.00.618.931 I llama_context: n_rs_seq      = 0
+0.00.618.931 I llama_context: n_outputs_max = 1
+0.00.618.932 I llama_context: n_ctx_seq (256) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
+0.00.618.952 D set_abort_callback: call
+0.00.619.239 I llama_context: Vulkan_Host  output buffer size =     0.95 MiB
+0.00.619.258 D llama_kv_cache: layer   0: filtered
+0.00.619.258 D llama_kv_cache: layer   1: filtered
+0.00.619.258 D llama_kv_cache: layer   2: filtered
+0.00.619.260 D llama_kv_cache: layer   3: dev = Vulkan0
+0.00.619.270 D llama_kv_cache: layer   4: filtered
+0.00.619.270 D llama_kv_cache: layer   5: filtered
+0.00.619.271 D llama_kv_cache: layer   6: filtered
+0.00.619.271 D llama_kv_cache: layer   7: dev = Vulkan0
+0.00.619.273 D llama_kv_cache: layer   8: filtered
+0.00.619.273 D llama_kv_cache: layer   9: filtered
+0.00.619.274 D llama_kv_cache: layer  10: filtered
+0.00.619.274 D llama_kv_cache: layer  11: dev = Vulkan0
+0.00.619.275 D llama_kv_cache: layer  12: filtered
+0.00.619.275 D llama_kv_cache: layer  13: filtered
+0.00.619.276 D llama_kv_cache: layer  14: filtered
+0.00.619.276 D llama_kv_cache: layer  15: dev = Vulkan0
+0.00.619.277 D llama_kv_cache: layer  16: filtered
+0.00.619.277 D llama_kv_cache: layer  17: filtered
+0.00.619.278 D llama_kv_cache: layer  18: filtered
+0.00.619.278 D llama_kv_cache: layer  19: dev = Vulkan0
+0.00.619.279 D llama_kv_cache: layer  20: filtered
+0.00.619.280 D llama_kv_cache: layer  21: filtered
+0.00.619.280 D llama_kv_cache: layer  22: filtered
+0.00.619.280 D llama_kv_cache: layer  23: dev = Vulkan0
+0.00.619.282 I llama_kv_cache:    Vulkan0 KV buffer size =     0.00 MiB
+0.00.619.285 I llama_kv_cache: size =    3.00 MiB (   256 cells,   6 layers,  1/1 seqs), K (f16):    1.50 MiB, V (f16):    1.50 MiB
+0.00.619.285 I llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
+0.00.619.286 I llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
+0.00.619.288 D llama_memory_recurrent, layer   0: dev = Vulkan0
+0.00.619.290 D llama_memory_recurrent, layer   1: dev = Vulkan0
+0.00.619.290 D llama_memory_recurrent, layer   2: dev = Vulkan0
+0.00.619.291 D llama_memory_recurrent: layer   3: skipped
+0.00.619.291 D llama_memory_recurrent, layer   4: dev = Vulkan0
+0.00.619.292 D llama_memory_recurrent, layer   5: dev = Vulkan0
+0.00.619.292 D llama_memory_recurrent, layer   6: dev = Vulkan0
+0.00.619.293 D llama_memory_recurrent: layer   7: skipped
+0.00.619.293 D llama_memory_recurrent, layer   8: dev = Vulkan0
+0.00.619.294 D llama_memory_recurrent, layer   9: dev = Vulkan0
+0.00.619.295 D llama_memory_recurrent, layer  10: dev = Vulkan0
+0.00.619.295 D llama_memory_recurrent: layer  11: skipped
+0.00.619.296 D llama_memory_recurrent, layer  12: dev = Vulkan0
+0.00.619.296 D llama_memory_recurrent, layer  13: dev = Vulkan0
+0.00.619.297 D llama_memory_recurrent, layer  14: dev = Vulkan0
+0.00.619.297 D llama_memory_recurrent: layer  15: skipped
+0.00.619.297 D llama_memory_recurrent, layer  16: dev = Vulkan0
+0.00.619.298 D llama_memory_recurrent, layer  17: dev = Vulkan0
+0.00.619.299 D llama_memory_recurrent, layer  18: dev = Vulkan0
+0.00.619.299 D llama_memory_recurrent: layer  19: skipped
+0.00.619.299 D llama_memory_recurrent, layer  20: dev = Vulkan0
+0.00.619.300 D llama_memory_recurrent, layer  21: dev = Vulkan0
+0.00.619.301 D llama_memory_recurrent, layer  22: dev = Vulkan0
+0.00.619.301 D llama_memory_recurrent: layer  23: skipped
+0.00.622.195 I llama_memory_recurrent:    Vulkan0 RS buffer size =    19.27 MiB
+0.00.622.203 I llama_memory_recurrent: size =   19.27 MiB (     1 cells,  24 layers,  1 seqs  0 rs_seq), R (f32):    1.27 MiB, S (f32):   18.00 MiB
+0.00.622.205 D llama_context: enumerating backends
+0.00.622.209 D llama_context: backend_ptrs.size() = 2
+0.00.622.209 I sched_reserve: reserving ...
+0.00.622.210 D sched_reserve: max_nodes = 10272
+0.00.624.260 D sched_reserve: reserving full memory module
+0.00.624.269 D sched_reserve: worst-case: n_tokens = 1, n_seqs = 1, n_outputs = 1
+0.00.624.271 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.625.424 I resolve_fused_ops: Flash Attention enabled
+0.00.625.425 I resolve_fused_ops: resolving fused Gated Delta Net support:
+0.00.625.426 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.626.301 I resolve_fused_ops: fused Gated Delta Net (autoregressive) enabled
+0.00.626.303 D graph_reserve: reserving a graph for ubatch with n_tokens =   16, n_seqs =  1, n_outputs =   16
+0.00.627.240 I resolve_fused_ops: fused Gated Delta Net (chunked) enabled
+0.00.627.241 I resolve_fused_ops: resolving fused Lightning Indexer support:
+0.00.627.242 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.628.168 I resolve_fused_ops: Lightning Indexer enabled
+0.00.628.170 I resolve_fused_ops: resolving fused DeepSeek V4 HC support:
+0.00.628.171 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.629.089 I resolve_fused_ops: fused DeepSeek V4 HC pre enabled
+0.00.629.091 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.630.001 I resolve_fused_ops: fused DeepSeek V4 HC comb enabled
+0.00.630.003 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.630.865 I resolve_fused_ops: fused DeepSeek V4 HC post enabled
+0.00.630.868 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.632.027 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.632.896 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.00.633.804 I sched_reserve:    Vulkan0 compute buffer size =     2.16 MiB
+0.00.633.808 I sched_reserve: Vulkan_Host compute buffer size =     0.04 MiB
+0.00.633.808 I sched_reserve: graph nodes  = 1375
+0.00.633.808 I sched_reserve: graph splits = 2
+0.00.633.809 I sched_reserve: reserve took 11.60 ms, sched copies = 1
+0.00.634.091 I common_memory_breakdown_print: | memory breakdown [MiB]                         | total   free    self   model   context   compute    unaccounted |
+0.00.634.093 I common_memory_breakdown_print: |   - Vulkan0 (RX 460 Graphics (RADV POLARIS11)) |  2048 = 1433 + (1460 =  1436 +      22 +       2) +        -846 |
+0.00.634.093 I common_memory_breakdown_print: |   - Host                                       |                  485 =   485 +       0 +       0                |
+0.00.680.226 I common_params_fit_impl: projected to use 1460 MiB of device memory vs. 1433 MiB of free device memory
+0.00.680.229 I common_params_fit_impl: cannot meet free memory target of 1024 MiB, need to reduce device memory by 1050 MiB
+0.00.680.230 I common_params_fit_impl: context size set by user to 256 -> no change
+0.00.680.283 W common_fit_params: failed to fit params to free device memory: n_gpu_layers already set by user to -2, abort
+0.00.680.288 I common_fit_params: fitting params to free memory took 0.52 seconds
+0.00.717.439 I llama_model_loader: loaded meta data with 33 key-value pairs and 320 tensors from MVP/artifacts/qwen35-teacher-text-24-bf16.gguf (version GGUF V3 (latest))
+0.00.717.450 I llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
+0.00.717.453 I llama_model_loader: - kv   0:                       general.architecture str              = qwen35
+0.00.717.454 I llama_model_loader: - kv   1:                               general.type str              = model
+0.00.717.454 I llama_model_loader: - kv   2:                               general.name str              = Echo Qwen35 teacher.Fmw91y
+0.00.717.455 I llama_model_loader: - kv   3:                         general.size_label str              = 752M
+0.00.717.456 I llama_model_loader: - kv   4:                         qwen35.block_count u32              = 24
+0.00.717.457 I llama_model_loader: - kv   5:                      qwen35.context_length u32              = 262144
+0.00.717.457 I llama_model_loader: - kv   6:                    qwen35.embedding_length u32              = 1024
+0.00.717.457 I llama_model_loader: - kv   7:                 qwen35.feed_forward_length u32              = 3584
+0.00.717.458 I llama_model_loader: - kv   8:                qwen35.attention.head_count u32              = 8
+0.00.717.458 I llama_model_loader: - kv   9:             qwen35.attention.head_count_kv u32              = 2
+0.00.717.465 I llama_model_loader: - kv  10:             qwen35.rope.dimension_sections arr[i32,4]       = [11, 11, 10, 0]
+0.00.717.472 I llama_model_loader: - kv  11:                      qwen35.rope.freq_base f32              = 10000000.000000
+0.00.717.473 I llama_model_loader: - kv  12:    qwen35.attention.layer_norm_rms_epsilon f32              = 0.000001
+0.00.717.473 I llama_model_loader: - kv  13:                qwen35.attention.key_length u32              = 256
+0.00.717.474 I llama_model_loader: - kv  14:              qwen35.attention.value_length u32              = 256
+0.00.717.474 I llama_model_loader: - kv  15:                          general.file_type u32              = 32
+0.00.717.475 I llama_model_loader: - kv  16:                     qwen35.ssm.conv_kernel u32              = 4
+0.00.717.475 I llama_model_loader: - kv  17:                      qwen35.ssm.state_size u32              = 128
+0.00.717.476 I llama_model_loader: - kv  18:                     qwen35.ssm.group_count u32              = 16
+0.00.717.476 I llama_model_loader: - kv  19:                  qwen35.ssm.time_step_rank u32              = 16
+0.00.717.476 I llama_model_loader: - kv  20:                      qwen35.ssm.inner_size u32              = 2048
+0.00.717.477 I llama_model_loader: - kv  21:             qwen35.full_attention_interval u32              = 4
+0.00.717.477 I llama_model_loader: - kv  22:                qwen35.rope.dimension_count u32              = 64
+0.00.717.477 I llama_model_loader: - kv  23:               general.quantization_version u32              = 2
+0.00.717.478 I llama_model_loader: - kv  24:                       tokenizer.ggml.model str              = gpt2
+0.00.717.478 I llama_model_loader: - kv  25:                         tokenizer.ggml.pre str              = qwen35
+0.00.742.328 I llama_model_loader: - kv  26:                      tokenizer.ggml.tokens arr[str,248320]  = ["!", "\"", "#", "$", "%", "&", "'", ...
+0.00.749.505 I llama_model_loader: - kv  27:                  tokenizer.ggml.token_type arr[i32,248320]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
+0.00.773.559 I llama_model_loader: - kv  28:                      tokenizer.ggml.merges arr[str,247587]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
+0.00.773.566 I llama_model_loader: - kv  29:                tokenizer.ggml.eos_token_id u32              = 248044
+0.00.773.567 I llama_model_loader: - kv  30:            tokenizer.ggml.padding_token_id u32              = 248044
+0.00.773.567 I llama_model_loader: - kv  31:               tokenizer.ggml.add_bos_token bool             = false
+0.00.773.571 I llama_model_loader: - kv  32:                    tokenizer.chat_template str              = {%- set image_count = namespace(value...
+0.00.773.572 I llama_model_loader: - type  f32:  133 tensors
+0.00.773.572 I llama_model_loader: - type bf16:  187 tensors
+0.00.773.574 I print_info: file format = GGUF V3 (latest)
+0.00.773.574 I print_info: file type   = BF16
+0.00.773.579 I print_info: file size   = 1.40 GiB (16.01 BPW) 
+0.00.773.689 I llama_prepare_model_devices: using device Vulkan0 (AMD Radeon RX 460 Graphics (RADV POLARIS11)) (0000:29:00.0) - 1453 MiB free
+0.00.912.104 D init_tokenizer: initializing tokenizer for type 2
+0.00.955.400 I load: 0 unused tokens
+0.00.955.415 D load: control token: 248075 '<tts_text_bos_single>' is not marked as EOG
+0.00.955.415 D load: control token: 248073 '<tts_text_bos>' is not marked as EOG
+0.00.955.416 D load: control token: 248072 '<tts_pad>' is not marked as EOG
+0.00.955.416 D load: control token: 248071 '<|audio_end|>' is not marked as EOG
+0.00.955.417 D load: control token: 248061 '<|fim_middle|>' is not marked as EOG
+0.00.955.417 D load: control token: 248055 '<|vision_pad|>' is not marked as EOG
+0.00.955.418 D load: control token: 248052 '<|quad_end|>' is not marked as EOG
+0.00.955.418 D load: control token: 248049 '<|box_start|>' is not marked as EOG
+0.00.955.418 D load: control token: 248048 '<|object_ref_end|>' is not marked as EOG
+0.00.955.419 D load: control token: 248045 '<|im_start|>' is not marked as EOG
+0.00.955.802 D load: control token: 248057 '<|video_pad|>' is not marked as EOG
+0.00.958.101 D load: control token: 248070 '<|audio_start|>' is not marked as EOG
+0.00.958.271 D load: control token: 248056 '<|image_pad|>' is not marked as EOG
+0.00.958.495 D load: control token: 248054 '<|vision_end|>' is not marked as EOG
+0.00.962.395 D load: control token: 248060 '<|fim_prefix|>' is not marked as EOG
+0.00.962.973 D load: control token: 248050 '<|box_end|>' is not marked as EOG
+0.00.970.041 D load: control token: 248074 '<tts_text_eod>' is not marked as EOG
+0.00.979.199 D load: control token: 248053 '<|vision_start|>' is not marked as EOG
+0.00.981.665 D load: control token: 248062 '<|fim_suffix|>' is not marked as EOG
+0.00.983.686 D load: control token: 248047 '<|object_ref_start|>' is not marked as EOG
+0.00.985.133 D load: control token: 248051 '<|quad_start|>' is not marked as EOG
+0.00.987.015 D load: control token: 248076 '<|audio_pad|>' is not marked as EOG
+0.01.006.213 I load: printing all EOG tokens:
+0.01.006.217 I load:   - 248044 ('<|endoftext|>')
+0.01.006.218 I load:   - 248046 ('<|im_end|>')
+0.01.006.218 I load:   - 248063 ('<|fim_pad|>')
+0.01.006.218 I load:   - 248064 ('<|repo_name|>')
+0.01.006.219 I load:   - 248065 ('<|file_sep|>')
+0.01.006.885 I load: special tokens cache size = 33
+0.01.091.486 I load: token to piece cache size = 1.7581 MB
+0.01.091.497 I print_info: arch                  = qwen35
+0.01.091.497 I print_info: vocab_only            = 0
+0.01.091.498 I print_info: no_alloc              = 0
+0.01.091.498 I print_info: n_ctx_train           = 262144
+0.01.091.499 I print_info: n_embd_inp            = 1024
+0.01.091.499 I print_info: n_embd                = 1024
+0.01.091.499 I print_info: n_embd_out            = 1024
+0.01.091.499 I print_info: n_layer               = 24
+0.01.091.500 I print_info: n_layer_all           = 24
+0.01.091.507 I print_info: n_head                = 8
+0.01.091.507 I print_info: n_head_kv             = 2
+0.01.091.508 I print_info: n_rot                 = 64
+0.01.091.508 I print_info: n_swa                 = 0
+0.01.091.508 I print_info: is_swa_any            = 0
+0.01.091.509 I print_info: n_embd_head_k         = 256
+0.01.091.509 I print_info: n_embd_head_v         = 256
+0.01.091.510 I print_info: n_gqa                 = 4
+0.01.091.511 I print_info: n_embd_k_gqa          = 512
+0.01.091.511 I print_info: n_embd_v_gqa          = 512
+0.01.091.512 I print_info: f_norm_eps            = 0.0e+00
+0.01.091.513 I print_info: f_norm_rms_eps        = 1.0e-06
+0.01.091.513 I print_info: f_clamp_kqv           = 0.0e+00
+0.01.091.514 I print_info: f_max_alibi_bias      = 0.0e+00
+0.01.091.514 I print_info: f_logit_scale         = 0.0e+00
+0.01.091.514 I print_info: f_attn_scale          = 0.0e+00
+0.01.091.514 I print_info: f_attn_value_scale    = 0.0000
+0.01.091.515 I print_info: n_ff                  = 3584
+0.01.091.515 I print_info: n_expert              = 0
+0.01.091.516 I print_info: n_expert_used         = 0
+0.01.091.516 I print_info: n_expert_groups       = 0
+0.01.091.516 I print_info: n_group_used          = 0
+0.01.091.516 I print_info: causal attn           = 1
+0.01.091.516 I print_info: pooling type          = -1
+0.01.091.517 I print_info: rope type             = 40
+0.01.091.517 I print_info: rope scaling          = linear
+0.01.091.518 I print_info: freq_base_train       = 10000000.0
+0.01.091.518 I print_info: freq_scale_train      = 1
+0.01.091.519 I print_info: n_ctx_orig_yarn       = 262144
+0.01.091.519 I print_info: rope_yarn_log_mul     = 0.0000
+0.01.091.519 I print_info: rope_finetuned        = unknown
+0.01.091.520 I print_info: mrope sections        = [11, 11, 10, 0]
+0.01.091.520 I print_info: ssm_d_conv            = 4
+0.01.091.520 I print_info: ssm_d_inner           = 2048
+0.01.091.520 I print_info: ssm_d_state           = 128
+0.01.091.521 I print_info: ssm_dt_rank           = 16
+0.01.091.521 I print_info: ssm_n_group           = 16
+0.01.091.521 I print_info: ssm_dt_b_c_rms        = 0
+0.01.091.522 I print_info: model type            = 0.8B
+0.01.091.522 I print_info: model params          = 752.39 M
+0.01.091.523 I print_info: general.name          = Echo Qwen35 teacher.Fmw91y
+0.01.091.524 I print_info: vocab type            = BPE
+0.01.091.524 I print_info: n_vocab               = 248320
+0.01.091.524 I print_info: n_merges              = 247587
+0.01.091.525 I print_info: BOS token             = 11 ','
+0.01.091.525 I print_info: EOS token             = 248044 '<|endoftext|>'
+0.01.091.525 I print_info: EOT token             = 248046 '<|im_end|>'
+0.01.091.525 I print_info: PAD token             = 248044 '<|endoftext|>'
+0.01.091.526 I print_info: LF token              = 198 'Ċ'
+0.01.091.526 I print_info: FIM PRE token         = 248060 '<|fim_prefix|>'
+0.01.091.526 I print_info: FIM SUF token         = 248062 '<|fim_suffix|>'
+0.01.091.526 I print_info: FIM MID token         = 248061 '<|fim_middle|>'
+0.01.091.526 I print_info: FIM PAD token         = 248063 '<|fim_pad|>'
+0.01.091.527 I print_info: FIM REP token         = 248064 '<|repo_name|>'
+0.01.091.527 I print_info: FIM SEP token         = 248065 '<|file_sep|>'
+0.01.091.527 I print_info: EOG token             = 248044 '<|endoftext|>'
+0.01.091.528 I print_info: EOG token             = 248046 '<|im_end|>'
+0.01.091.528 I print_info: EOG token             = 248063 '<|fim_pad|>'
+0.01.091.528 I print_info: EOG token             = 248064 '<|repo_name|>'
+0.01.091.528 I print_info: EOG token             = 248065 '<|file_sep|>'
+0.01.091.528 I print_info: max token length      = 256
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+0.01.093.025 D create_tensor: loading tensor blk.17.ffn_up.weight
+0.01.093.030 D create_tensor: loading tensor blk.18.attn_norm.weight
+0.01.093.038 D create_tensor: loading tensor blk.18.post_attention_norm.weight
+0.01.093.044 D create_tensor: loading tensor blk.18.attn_qkv.weight
+0.01.093.049 D create_tensor: loading tensor blk.18.attn_gate.weight
+0.01.093.054 D create_tensor: loading tensor blk.18.ssm_conv1d.weight
+0.01.093.059 D create_tensor: loading tensor blk.18.ssm_dt.bias
+0.01.093.065 D create_tensor: loading tensor blk.18.ssm_a
+0.01.093.070 D create_tensor: loading tensor blk.18.ssm_beta.weight
+0.01.093.075 D create_tensor: loading tensor blk.18.ssm_alpha.weight
+0.01.093.080 D create_tensor: loading tensor blk.18.ssm_norm.weight
+0.01.093.085 D create_tensor: loading tensor blk.18.ssm_out.weight
+0.01.093.090 D create_tensor: loading tensor blk.18.ffn_gate.weight
+0.01.093.097 D create_tensor: loading tensor blk.18.ffn_down.weight
+0.01.093.103 D create_tensor: loading tensor blk.18.ffn_up.weight
+0.01.093.108 D create_tensor: loading tensor blk.19.attn_norm.weight
+0.01.093.113 D create_tensor: loading tensor blk.19.post_attention_norm.weight
+0.01.093.120 D create_tensor: loading tensor blk.19.attn_q.weight
+0.01.093.126 D create_tensor: loading tensor blk.19.attn_k.weight
+0.01.093.131 D create_tensor: loading tensor blk.19.attn_v.weight
+0.01.093.142 D create_tensor: loading tensor blk.19.attn_output.weight
+0.01.093.148 D create_tensor: loading tensor blk.19.attn_q_norm.weight
+0.01.093.153 D create_tensor: loading tensor blk.19.attn_k_norm.weight
+0.01.093.158 D create_tensor: loading tensor blk.19.ffn_gate.weight
+0.01.093.163 D create_tensor: loading tensor blk.19.ffn_down.weight
+0.01.093.170 D create_tensor: loading tensor blk.19.ffn_up.weight
+0.01.093.175 D create_tensor: loading tensor blk.20.attn_norm.weight
+0.01.093.180 D create_tensor: loading tensor blk.20.post_attention_norm.weight
+0.01.093.186 D create_tensor: loading tensor blk.20.attn_qkv.weight
+0.01.093.191 D create_tensor: loading tensor blk.20.attn_gate.weight
+0.01.093.196 D create_tensor: loading tensor blk.20.ssm_conv1d.weight
+0.01.093.201 D create_tensor: loading tensor blk.20.ssm_dt.bias
+0.01.093.206 D create_tensor: loading tensor blk.20.ssm_a
+0.01.093.212 D create_tensor: loading tensor blk.20.ssm_beta.weight
+0.01.093.217 D create_tensor: loading tensor blk.20.ssm_alpha.weight
+0.01.093.222 D create_tensor: loading tensor blk.20.ssm_norm.weight
+0.01.093.229 D create_tensor: loading tensor blk.20.ssm_out.weight
+0.01.093.234 D create_tensor: loading tensor blk.20.ffn_gate.weight
+0.01.093.239 D create_tensor: loading tensor blk.20.ffn_down.weight
+0.01.093.245 D create_tensor: loading tensor blk.20.ffn_up.weight
+0.01.093.251 D create_tensor: loading tensor blk.21.attn_norm.weight
+0.01.093.257 D create_tensor: loading tensor blk.21.post_attention_norm.weight
+0.01.093.263 D create_tensor: loading tensor blk.21.attn_qkv.weight
+0.01.093.268 D create_tensor: loading tensor blk.21.attn_gate.weight
+0.01.093.274 D create_tensor: loading tensor blk.21.ssm_conv1d.weight
+0.01.093.279 D create_tensor: loading tensor blk.21.ssm_dt.bias
+0.01.093.285 D create_tensor: loading tensor blk.21.ssm_a
+0.01.093.292 D create_tensor: loading tensor blk.21.ssm_beta.weight
+0.01.093.298 D create_tensor: loading tensor blk.21.ssm_alpha.weight
+0.01.093.303 D create_tensor: loading tensor blk.21.ssm_norm.weight
+0.01.093.308 D create_tensor: loading tensor blk.21.ssm_out.weight
+0.01.093.314 D create_tensor: loading tensor blk.21.ffn_gate.weight
+0.01.093.319 D create_tensor: loading tensor blk.21.ffn_down.weight
+0.01.093.325 D create_tensor: loading tensor blk.21.ffn_up.weight
+0.01.093.331 D create_tensor: loading tensor blk.22.attn_norm.weight
+0.01.093.337 D create_tensor: loading tensor blk.22.post_attention_norm.weight
+0.01.093.343 D create_tensor: loading tensor blk.22.attn_qkv.weight
+0.01.093.349 D create_tensor: loading tensor blk.22.attn_gate.weight
+0.01.093.356 D create_tensor: loading tensor blk.22.ssm_conv1d.weight
+0.01.093.362 D create_tensor: loading tensor blk.22.ssm_dt.bias
+0.01.093.368 D create_tensor: loading tensor blk.22.ssm_a
+0.01.093.374 D create_tensor: loading tensor blk.22.ssm_beta.weight
+0.01.093.379 D create_tensor: loading tensor blk.22.ssm_alpha.weight
+0.01.093.385 D create_tensor: loading tensor blk.22.ssm_norm.weight
+0.01.093.391 D create_tensor: loading tensor blk.22.ssm_out.weight
+0.01.093.397 D create_tensor: loading tensor blk.22.ffn_gate.weight
+0.01.093.403 D create_tensor: loading tensor blk.22.ffn_down.weight
+0.01.093.408 D create_tensor: loading tensor blk.22.ffn_up.weight
+0.01.093.414 D create_tensor: loading tensor blk.23.attn_norm.weight
+0.01.093.422 D create_tensor: loading tensor blk.23.post_attention_norm.weight
+0.01.093.432 D create_tensor: loading tensor blk.23.attn_q.weight
+0.01.093.438 D create_tensor: loading tensor blk.23.attn_k.weight
+0.01.093.444 D create_tensor: loading tensor blk.23.attn_v.weight
+0.01.093.458 D create_tensor: loading tensor blk.23.attn_output.weight
+0.01.093.464 D create_tensor: loading tensor blk.23.attn_q_norm.weight
+0.01.093.471 D create_tensor: loading tensor blk.23.attn_k_norm.weight
+0.01.093.477 D create_tensor: loading tensor blk.23.ffn_gate.weight
+0.01.093.483 D create_tensor: loading tensor blk.23.ffn_down.weight
+0.01.093.489 D create_tensor: loading tensor blk.23.ffn_up.weight
+0.01.094.242 D done_getting_tensors: tensor 'token_embd.weight' (bf16) (and 0 others) cannot be used with preferred buffer type CPU_REPACK, using CPU instead
+0.01.225.551 I load_tensors: offloading output layer to GPU
+0.01.225.554 I load_tensors: offloading 23 repeating layers to GPU
+0.01.225.555 I load_tensors: offloaded 25/25 layers to GPU
+0.01.225.560 I load_tensors:   CPU_Mapped model buffer size =   485.00 MiB
+0.01.225.562 I load_tensors:      Vulkan0 model buffer size =  1436.03 MiB
+0.01.834.200 I cmn  common_init_: added <|endoftext|> logit bias = -inf
+0.01.834.204 I cmn  common_init_: added <|im_end|> logit bias = -inf
+0.01.834.205 I cmn  common_init_: added <|fim_pad|> logit bias = -inf
+0.01.834.205 I cmn  common_init_: added <|repo_name|> logit bias = -inf
+0.01.834.206 I cmn  common_init_: added <|file_sep|> logit bias = -inf
+0.01.834.254 I llama_context: constructing llama_context
+0.01.834.256 I llama_context: n_seq_max     = 1
+0.01.834.256 I llama_context: n_ctx         = 256
+0.01.834.256 I llama_context: n_ctx_seq     = 256
+0.01.834.257 I llama_context: n_batch       = 128
+0.01.834.257 I llama_context: n_ubatch      = 1
+0.01.834.257 I llama_context: causal_attn   = 1
+0.01.834.258 I llama_context: flash_attn    = auto
+0.01.834.258 I llama_context: kv_unified    = false
+0.01.834.263 I llama_context: freq_base     = 10000000.0
+0.01.834.263 I llama_context: freq_scale    = 1
+0.01.834.264 I llama_context: n_rs_seq      = 0
+0.01.834.264 I llama_context: n_outputs_max = 1
+0.01.834.264 I llama_context: n_ctx_seq (256) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
+0.01.834.284 D set_abort_callback: call
+0.01.834.557 I llama_context: Vulkan_Host  output buffer size =     0.95 MiB
+0.01.834.567 D llama_kv_cache: layer   0: filtered
+0.01.834.568 D llama_kv_cache: layer   1: filtered
+0.01.834.568 D llama_kv_cache: layer   2: filtered
+0.01.834.570 D llama_kv_cache: layer   3: dev = Vulkan0
+0.01.834.576 D llama_kv_cache: layer   4: filtered
+0.01.834.577 D llama_kv_cache: layer   5: filtered
+0.01.834.577 D llama_kv_cache: layer   6: filtered
+0.01.834.577 D llama_kv_cache: layer   7: dev = Vulkan0
+0.01.834.579 D llama_kv_cache: layer   8: filtered
+0.01.834.579 D llama_kv_cache: layer   9: filtered
+0.01.834.579 D llama_kv_cache: layer  10: filtered
+0.01.834.580 D llama_kv_cache: layer  11: dev = Vulkan0
+0.01.834.582 D llama_kv_cache: layer  12: filtered
+0.01.834.582 D llama_kv_cache: layer  13: filtered
+0.01.834.582 D llama_kv_cache: layer  14: filtered
+0.01.834.582 D llama_kv_cache: layer  15: dev = Vulkan0
+0.01.834.584 D llama_kv_cache: layer  16: filtered
+0.01.834.584 D llama_kv_cache: layer  17: filtered
+0.01.834.584 D llama_kv_cache: layer  18: filtered
+0.01.834.585 D llama_kv_cache: layer  19: dev = Vulkan0
+0.01.834.587 D llama_kv_cache: layer  20: filtered
+0.01.834.587 D llama_kv_cache: layer  21: filtered
+0.01.834.587 D llama_kv_cache: layer  22: filtered
+0.01.834.587 D llama_kv_cache: layer  23: dev = Vulkan0
+0.01.834.920 I llama_kv_cache:    Vulkan0 KV buffer size =     3.00 MiB
+0.01.835.698 I llama_kv_cache: size =    3.00 MiB (   256 cells,   6 layers,  1/1 seqs), K (f16):    1.50 MiB, V (f16):    1.50 MiB
+0.01.835.702 I llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
+0.01.835.702 I llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
+0.01.835.704 D llama_memory_recurrent, layer   0: dev = Vulkan0
+0.01.835.705 D llama_memory_recurrent, layer   1: dev = Vulkan0
+0.01.835.706 D llama_memory_recurrent, layer   2: dev = Vulkan0
+0.01.835.706 D llama_memory_recurrent: layer   3: skipped
+0.01.835.707 D llama_memory_recurrent, layer   4: dev = Vulkan0
+0.01.835.707 D llama_memory_recurrent, layer   5: dev = Vulkan0
+0.01.835.708 D llama_memory_recurrent, layer   6: dev = Vulkan0
+0.01.835.708 D llama_memory_recurrent: layer   7: skipped
+0.01.835.708 D llama_memory_recurrent, layer   8: dev = Vulkan0
+0.01.835.709 D llama_memory_recurrent, layer   9: dev = Vulkan0
+0.01.835.709 D llama_memory_recurrent, layer  10: dev = Vulkan0
+0.01.835.710 D llama_memory_recurrent: layer  11: skipped
+0.01.835.710 D llama_memory_recurrent, layer  12: dev = Vulkan0
+0.01.835.711 D llama_memory_recurrent, layer  13: dev = Vulkan0
+0.01.835.712 D llama_memory_recurrent, layer  14: dev = Vulkan0
+0.01.835.713 D llama_memory_recurrent: layer  15: skipped
+0.01.835.713 D llama_memory_recurrent, layer  16: dev = Vulkan0
+0.01.835.714 D llama_memory_recurrent, layer  17: dev = Vulkan0
+0.01.835.714 D llama_memory_recurrent, layer  18: dev = Vulkan0
+0.01.835.715 D llama_memory_recurrent: layer  19: skipped
+0.01.835.715 D llama_memory_recurrent, layer  20: dev = Vulkan0
+0.01.835.715 D llama_memory_recurrent, layer  21: dev = Vulkan0
+0.01.835.716 D llama_memory_recurrent, layer  22: dev = Vulkan0
+0.01.835.716 D llama_memory_recurrent: layer  23: skipped
+0.01.842.011 I llama_memory_recurrent:    Vulkan0 RS buffer size =    19.27 MiB
+0.01.842.017 I llama_memory_recurrent: size =   19.27 MiB (     1 cells,  24 layers,  1 seqs  0 rs_seq), R (f32):    1.27 MiB, S (f32):   18.00 MiB
+0.01.842.019 D llama_context: enumerating backends
+0.01.842.023 D llama_context: backend_ptrs.size() = 2
+0.01.842.023 I sched_reserve: reserving ...
+0.01.842.024 D sched_reserve: max_nodes = 10272
+0.01.843.240 D sched_reserve: reserving full memory module
+0.01.843.248 D sched_reserve: worst-case: n_tokens = 1, n_seqs = 1, n_outputs = 1
+0.01.843.249 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.844.351 I resolve_fused_ops: Flash Attention enabled
+0.01.844.353 I resolve_fused_ops: resolving fused Gated Delta Net support:
+0.01.844.354 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.845.277 I resolve_fused_ops: fused Gated Delta Net (autoregressive) enabled
+0.01.845.281 D graph_reserve: reserving a graph for ubatch with n_tokens =   16, n_seqs =  1, n_outputs =   16
+0.01.846.204 I resolve_fused_ops: fused Gated Delta Net (chunked) enabled
+0.01.846.207 I resolve_fused_ops: resolving fused Lightning Indexer support:
+0.01.846.208 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.847.085 I resolve_fused_ops: Lightning Indexer enabled
+0.01.847.088 I resolve_fused_ops: resolving fused DeepSeek V4 HC support:
+0.01.847.089 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.847.971 I resolve_fused_ops: fused DeepSeek V4 HC pre enabled
+0.01.847.974 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.848.841 I resolve_fused_ops: fused DeepSeek V4 HC comb enabled
+0.01.848.843 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.849.716 I resolve_fused_ops: fused DeepSeek V4 HC post enabled
+0.01.849.720 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.851.209 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.852.330 D graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
+0.01.853.437 I sched_reserve:    Vulkan0 compute buffer size =     2.16 MiB
+0.01.853.441 I sched_reserve: Vulkan_Host compute buffer size =     0.04 MiB
+0.01.853.441 I sched_reserve: graph nodes  = 1375
+0.01.853.441 I sched_reserve: graph splits = 2
+0.01.853.443 I sched_reserve: reserve took 11.42 ms, sched copies = 1
+0.01.853.549 D set_adapters_lora: adapters = (nil)
+0.01.853.551 D adapters_lora_are_same: adapters = (nil)
+0.01.853.552 I cmn  common_init_: warming up the model with an empty run - please wait ... (--no-warmup to disable)

MVP/evidence/vulkan-devices.log

@@ -0,0 +1,2 @@
+Available devices:
+  Vulkan0: AMD Radeon RX 460 Graphics (RADV POLARIS11) (2048 MiB, 1504 MiB free)

MVP/goal0.txt

@@ -0,0 +1,117 @@
+# TASK: Empirical Pruning of Qwen/Qwen3.5-0.8B-Base to 330-450M Parameters ((Not approximately, but exactly. It must be one of these two, or have a parameter within these two ranges.))
+
+## 0. Ground Truth — Do Not Assume Numbers, Compute Them
+Qwen/Qwen3.5-0.8B-Base is a MULTIMODAL model (architecture: Qwen3_5ForConditionalGeneration),
+with config split into `vision_config` and `text_config`. This task prunes the TEXT backbone only;
+the vision tower is dropped entirely.
+
+Real text_config fields (verified from HF config.json):
+- hidden_size: 1024
+- intermediate_size: 3584   (NOT 2816)
+- num_hidden_layers: 24
+- layer_types: ["linear_attention"]*3 + ["full_attention"] repeated 6x (24 entries)
+- full_attention_interval: 4
+- head_dim: 256 (full attention)
+- linear_key_head_dim: 128, linear_conv_kernel_dim: 4
+- linear_num_key_heads: 16, linear_num_value_heads: 32 (verify against live config — sources disagree,
+  model card text says 16/16, raw config.json says 16 key / 32 value heads; trust the live config.json
+  you load at runtime, not any cached number in this document)
+- vocab / tied embedding: 248,320 tokens × 1024 = ~254.3M params — this is FIXED regardless of
+  how many layers you keep, since it's tied to the LM head.
+
+## 1. Feasibility Check (run BEFORE deciding any target layer count)
+Load the real model and measure actual per-layer parameter counts. Do not estimate by hand or trust
+any prior document's math — prior estimates for this exact model have been off by 100M+.
+
+```python
+from transformers import AutoModelForCausalLM, AutoConfig
+import torch
+
+config = AutoConfig.from_pretrained("Qwen/Qwen3.5-0.8B-Base", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3.5-0.8B-Base", trust_remote_code=True, torch_dtype=torch.bfloat16
+)
+# Isolate the text backbone only (drop vision tower)
+text_model = model.model.language_model if hasattr(model.model, "language_model") else model.model
+
+embed_params = sum(p.numel() for p in text_model.embed_tokens.parameters())
+layer_params = [sum(p.numel() for p in layer.parameters()) for layer in text_model.layers]
+
+print(f"Embedding (tied) params: {embed_params/1e6:.1f}M")
+for i, lp in enumerate(layer_params):
+    print(f"Layer {i} ({config.text_config.layer_types[i]}): {lp/1e6:.2f}M")
+
+# Compute how many layers fit a given total budget
+target_min, target_max = 330_000_000, 350_000_000
+budget = target_max - embed_params
+cum = 0
+for i, lp in enumerate(layer_params):
+    cum += lp
+    if cum > budget:
+        print(f"With hidden_size unchanged, only ~{i} layers fit under {target_max/1e6:.0f}M total.")
+        break
+
+```
+
+Run this first. If the number of layers that fit is too small to preserve at least one complete
+hybrid block (3 linear_attention + 1 full_attention = 4 layers minimum for the architecture to
+still contain a full-attention layer at all), the 330-350M target is not reachable via depth
+pruning alone with hidden_size=1024 fixed. In that case go to Step 2b (width pruning) instead of
+Step 2a (depth-only).
+
+## 2a. Depth-Only Pruning (if Step 1 shows ≥4-8 layers fit the budget)
+
+Keep the first N layers, where N is a multiple of 4 (to preserve complete hybrid blocks) closest
+to (but not exceeding) the layer count computed in Step 1. Do not use activation-norm-based layer
+selection — codefuse-ai's F2LLM-v2 paper (arXiv:2603.19223) reports this underperforms simply
+keeping the first N layers for their pruning pipeline; apply the same simple-truncation strategy
+here.
+
+```python
+config.text_config.num_hidden_layers = N
+config.text_config.layer_types = config.text_config.layer_types[:N]
+
+```
+
+Transfer weights for layers[:N] as-is. Transfer embed_tokens, final norm, and tied lm_head intact
+(do not slice — hidden_size is unchanged in this path).
+
+## 2b. Width + Depth Pruning (if Step 1 shows depth pruning alone can't reach 330-350M)
+
+Follow F2LLM-v2's actual 3-dimension approach (arXiv:2603.19223, Section 3.3):
+
+1. Prune num_hidden_layers by keeping first N layers (same as 2a).
+2. Additionally prune hidden_size and intermediate_size using activation-norm-based row/column
+selection on a calibration set — rank neurons/dimensions by mean L2 activation norm, keep the
+top-scoring indices, slice all weight matrices consistently (q/k/v/gate/up/down projections,
+plus norms) to match the new hidden_size.
+3. Note: reducing hidden_size on this architecture also changes the GatedDeltaNet recurrent state
+width (key/value head dims are defined relative to hidden_size in the live config — re-derive
+linear_key_head_dim / linear_num_key_heads / linear_num_value_heads consistently after pruning;
+do not assume they scale linearly without checking the actual attention module's forward pass).
+4. Re-run the Step 1 measurement script against the pruned checkpoint to confirm the actual total
+before proceeding to distillation — do not trust the arithmetic prediction.
+
+## 3. Knowledge Distillation
+
+Teacher: frozen Qwen/Qwen3.5-0.8B-Base text backbone (bfloat16, eval mode).
+Student: the pruned checkpoint from Step 2a or 2b.
+Loss: standard next-token cross-entropy + MSE between teacher and student final hidden states
+(sequence representation), matching F2LLM-v2's knowledge distillation ablation (Table 4), which
+shows a consistent, measurable performance drop when distillation is omitted — do not skip it.
+Optimizer: AdamW, cosine schedule with warmup — tune lr/steps empirically; no external source
+gives a validated hyperparameter set for this specific pruned architecture, so treat any lr/steps
+figures as a starting point requiring empirical validation, not a fixed target.
+
+## 4. Validation
+
+Assert final total parameter count is inside [330_000_000, 350_000_000] using the Step 1 script
+against the final checkpoint (not a predicted number). If it falls outside the range, adjust N
+(depth) or the width-pruning target dimension and re-measure — do not adjust the target range to
+match whatever number you got.
+
+## 5. Commit
+
+Break each step into phases and make atomic commits.
+Making atomic commits makes it easier to track history and makes progress tracking much better.
+Subsequently, you must write the progress to PROGRESS.md and track it from there; checkmarks are added to the boxes as each task is completed.

MVP/test_multimodal_forward.py

@@ -0,0 +1,72 @@
+import sys, traceback
+print("STEP 0: imports", flush=True)
+import torch
+from transformers import Qwen3_5ForConditionalGeneration, AutoTokenizer, Qwen3VLProcessor, Qwen2VLImageProcessor, Qwen3VLVideoProcessor
+
+MODEL_DIR = "/home/void0x14/Documents/echo/MVP/artifacts/qwen35-distilled-n4-multimodal"
+
+print("STEP 1: tokenizer", flush=True)
+tok = AutoTokenizer.from_pretrained(MODEL_DIR)
+print("  image_token_id:", getattr(tok, "image_token_id", None), flush=True)
+print("  video_token_id:", getattr(tok, "video_token_id", None), flush=True)
+print("  pad:", tok.pad_token, flush=True)
+
+print("STEP 2: image processor", flush=True)
+img_pp = Qwen2VLImageProcessor.from_pretrained(MODEL_DIR)
+
+print("STEP 3: video processor", flush=True)
+try:
+    vid_pp = Qwen3VLVideoProcessor.from_pretrained(MODEL_DIR)
+    print("  video processor OK", flush=True)
+except Exception as e:
+    print("  video processor FAIL:", type(e).__name__, str(e)[:200], flush=True)
+    vid_pp = None
+
+print("STEP 4: processor bypass", flush=True)
+from transformers import AutoConfig
+cfg = AutoConfig.from_pretrained(MODEL_DIR)
+print("  cfg image_token_id:", cfg.image_token_id, flush=True)
+proc = Qwen3VLProcessor.__new__(Qwen3VLProcessor)
+proc.image_token = "<|image_pad|>"
+proc.video_token = "<|video_pad|>"
+proc.vision_start_token = "<|vision_start|>"
+proc.vision_end_token = "<|vision_end|>"
+proc.image_token_id = cfg.image_token_id
+proc.video_token_id = cfg.video_token_id
+proc.vision_start_token_id = cfg.vision_start_token_id
+proc.vision_end_token_id = cfg.vision_end_token_id
+proc.tokenizer = tok
+proc.image_processor = img_pp
+proc.video_processor = vid_pp
+proc.chat_template = tok.chat_template
+print("  processor bypass OK", flush=True)
+
+print("TOKEN SABITLERI KURULDU", flush=True)
+
+print("STEP 5: load model", flush=True)
+model = Qwen3_5ForConditionalGeneration.from_pretrained(MODEL_DIR, torch_dtype=torch.float32)
+model.eval()
+print("  model loaded", flush=True)
+
+print("STEP 6: build inputs", flush=True)
+import numpy as np
+from PIL import Image
+img = Image.new("RGB", (224, 224), (120, 60, 200))
+
+messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Bu resimde ne var?"}]}]
+text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+print("  chat text:", text[:120], flush=True)
+
+inputs = proc(text=[text], images=[img], return_tensors="pt")
+print("  input keys:", list(inputs.keys()), flush=True)
+print("  input_ids shape:", inputs["input_ids"].shape, flush=True)
+print("  pixel_values shape:", inputs["pixel_values"].shape, flush=True)
+
+print("STEP 7: forward", flush=True)
+with torch.no_grad():
+    out = model(**inputs)
+    print("LOGITS:", tuple(out.logits.shape), flush=True)
+    pred = out.logits[0, -1].argmax().item()
+    print("  last token pred:", pred, tok.decode([pred])[:50], flush=True)
+
+print("MULTIMODAL FORWARD OK", flush=True)