Instructions to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("feature-extraction", model="mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless")
model = AutoModel.from_pretrained("mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
# Run inference directly in the terminal:
llama cli -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
# Run inference directly in the terminal:
llama cli -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Use Docker

docker model run hf.co/mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

LM Studio
Jan
Ollama
How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Ollama:
```
ollama run hf.co/mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
```
Unsloth Desktop

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Docker Model Runner:
```
docker model run hf.co/mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
```

Lemonade

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Run and chat with the model

lemonade run user.qwen3.5-4l-vocab40k-en-ko-headless-Q8_0

List all available models

lemonade list

Hermes Agent

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

mp-juuuns commited on 4 days ago

Commit

1bc5bc2

verified ·

1 Parent(s): 7121fa8

Put the full metric comparison at the top; strip local paths; drop stale manifests

Browse files

Comparison: the card never placed this root against the field. It now does, at the top, on every metric the protocol measured. Quality rank 3 of 65 (a six-arm tie inside one median seed SD), latency 52/65, energy 52/65, RSS 40/65, file 31/65. New figure 10 shows all 65 arms per metric.

Local paths: seven manifests carried /home/mp_ubuntu/Projects/... absolute paths into a public repository. 24 occurrences rewritten to repo-relative; no other content changed.

Deleted, all stale or wrong:
release_manifest.json - asserts the OLD repo id and the OLD root's weight sha256
benchmark/summary.json - superseded-lineage numbers; also claims a test split this repo
benchmark/semeval-transfer-summary.json does not have. Byte-identical triplicate.
benchmark/figures/source_summary.json
benchmark/figures/manifest.json - schema standalone4l-v128k-figures-v1, describes a
benchmark/figures/cross_task/manifest.json figure set the current card does not use
benchmark/figures/cross_task/source_summary.json

Files changed (17) hide show

.gitattributes +1 -0
benchmark/figures/10_root_on_every_metric.png +3 -0
benchmark/figures/cross_task/manifest.json +0 -158
benchmark/figures/cross_task/source_summary.json +0 -972
benchmark/figures/fig10.py +92 -0
benchmark/figures/manifest.json +0 -14
benchmark/figures/source_summary.json +0 -891
benchmark/semeval-transfer-summary.json +0 -891
benchmark/summary.json +0 -891
finalization_manifest.json +2 -2
provenance/base/finalization_manifest.json +2 -2
provenance/base/training_manifest.json +4 -4
provenance/task_agnostic_stages/24to8.json +4 -4
provenance/task_agnostic_stages/6to4.json +4 -4
provenance/task_agnostic_stages/8to6.json +4 -4
release_manifest.json +0 -675
training_manifest.json +4 -4

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+benchmark/figures/10_root_on_every_metric.png filter=lfs diff=lfs merge=lfs -text

benchmark/figures/10_root_on_every_metric.png ADDED Viewed

Git LFS Details

SHA256: 56a7353a71dd727077a420fd39a97b21742b8af87fabcbd4d4cdbfe09f5a0b40
Pointer size: 131 Bytes
Size of remote file: 158 kB

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benchmark/figures/cross_task/source_summary.json DELETED Viewed

@@ -1,972 +0,0 @@
-{
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-      "parameters": 752407360,
-      "peak_cuda_allocated_bytes": {
-        "mean": 7635339264.0,
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-        "mean": 0.0303030303030303,
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-      "test_articles": 55,
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-        "mean": 0.586362602903109,
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-        "sample_sd": 0.005979023788731576
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-      "total_wall_seconds": {
-        "mean": 12842.063612925334,
-        "n": 3,
-        "sample_sd": 94.80225434811064
-      }
-    },
-    {
-      "arm": "qwen35-c8q8l-commonhead",
-      "label": "8L",
-      "layers": 8,
-      "lineage_note": "same five-epoch common-head protocol",
-      "macro_f1": {
-        "mean": 0.5366873350531403,
-        "n": 3,
-        "sample_sd": 0.017264532146217884
-      },
-      "micro_f1": {
-        "mean": 0.6238830315003392,
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-      "peak_cuda_allocated_bytes": {
-        "mean": 4257894400.0,
-        "n": 3,
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-      "subset_exact_match": {
-        "mean": 0.006060606060606061,
-        "n": 3,
-        "sample_sd": 0.01049727762162956
-      },
-      "test_articles": 55,
-      "test_seconds_per_article": {
-        "mean": 0.206657676709195,
-        "n": 3,
-        "sample_sd": 0.002071265185964238
-      },
-      "total_wall_seconds": {
-        "mean": 4291.582853937677,
-        "n": 3,
-        "sample_sd": 44.49609583978631
-      }
-    },
-    {
-      "arm": "qwen35-standalone4l-commonhead",
-      "label": "4L",
-      "layers": 4,
-      "lineage_note": "same five-epoch common-head protocol",
-      "macro_f1": {
-        "mean": 0.5875679559555907,
-        "n": 3,
-        "sample_sd": 0.007165753447433348
-      },
-      "micro_f1": {
-        "mean": 0.647978512363484,
-        "n": 3,
-        "sample_sd": 0.0018739735696546695
-      },
-      "parameters": 210903360,
-      "peak_cuda_allocated_bytes": {
-        "mean": 2143655936.0,
-        "n": 3,
-        "sample_sd": 0.0
-      },
-      "subset_exact_match": {
-        "mean": 0.006060606060606061,
-        "n": 3,
-        "sample_sd": 0.01049727762162956
-      },
-      "test_articles": 55,
-      "test_seconds_per_article": {
-        "mean": 0.08543555729062946,
-        "n": 3,
-        "sample_sd": 0.0005980667826383295
-      },
-      "total_wall_seconds": {
-        "mean": 1643.6388439506602,
-        "n": 3,
-        "sample_sd": 9.226529522850198
-      }
-    }
-  ],
-  "schema": "qwen35-4l-cross-task-base-card-figures-v1",
-  "status": "complete",
-  "task_free_compression_ladder": [
-    {
-      "layers": 24,
-      "name": "24L upstream text teacher",
-      "parameters": 752393024,
-      "weight_bytes": 1504825632,
-      "weight_mib": 1435.1135559082031
-    },
-    {
-      "layers": 8,
-      "name": "8L task-free KD",
-      "parameters": 420318144,
-      "weight_bytes": 840647880,
-      "weight_mib": 801.7042922973633
-    },
-    {
-      "layers": 6,
-      "name": "6L task-free KD",
-      "parameters": 377207424,
-      "weight_bytes": 754423424,
-      "weight_mib": 719.4742431640625
-    },
-    {
-      "layers": 4,
-      "name": "4L task-free KD root",
-      "parameters": 334096704,
-      "weight_bytes": 668198976,
-      "weight_mib": 637.2442016601562
-    }
-  ],
-  "uno_q_controlled_depth": {
-    "boundary": "Matched V128k/F16 runtime comparison. The 4L arm is the historical early-path proxy, not the final common-head quality model.",
-    "claim_boundaries": [
-      "Only layer_effect_primary is used for same-vocabulary F16 depth-effect estimates.",
-      "Only vocabulary_effect_primary is used for exact-remap vocabulary-effect estimates.",
-      "Operational and cross-generation groups are descriptive because precision, heads, layer maps, training lineages, or architectures differ.",
-      "llama-bench embedding-mode latency measures backbone/classifier compute but does not validate evidence-span quality.",
-      "CPU occupancy is instantaneous saturation; core-seconds is a compute-duration proxy, not measured energy.",
-      "No power meter was used, so energy consumption is not reported.",
-      "Temperature is device- and ambient-specific and is descriptive, not a universal hardware constant."
-    ],
-    "rows": [
-      {
-        "layers": 8,
-        "metric": "artifact_mib",
-        "metric_label": "Artifact size",
-        "model": "8L",
-        "normalized_8l_percent": 100.0
-      },
-      {
-        "layers": 6,
-        "metric": "artifact_mib",
-        "metric_label": "Artifact size",
-        "model": "6L",
-        "normalized_8l_percent": 85.59176355424236
-      },
-      {
-        "layers": 4,
-        "metric": "artifact_mib",
-        "metric_label": "Artifact size",
-        "model": "4L proxy",
-        "normalized_8l_percent": 72.2607217304372
-      },
-      {
-        "layers": 8,
-        "metric": "latency_pp128_ms",
-        "metric_label": "PP128 latency",
-        "model": "8L",
-        "normalized_8l_percent": 100.0
-      },
-      {
-        "layers": 6,
-        "metric": "latency_pp128_ms",
-        "metric_label": "PP128 latency",
-        "model": "6L",
-        "normalized_8l_percent": 76.36565126547553
-      },
-      {
-        "layers": 4,
-        "metric": "latency_pp128_ms",
-        "metric_label": "PP128 latency",
-        "model": "4L proxy",
-        "normalized_8l_percent": 50.16216836129361
-      },
-      {
-        "layers": 8,
-        "metric": "peak_rss_mib",
-        "metric_label": "Peak RSS",
-        "model": "8L",
-        "normalized_8l_percent": 100.0
-      },
-      {
-        "layers": 6,
-        "metric": "peak_rss_mib",
-        "metric_label": "Peak RSS",
-        "model": "6L",
-        "normalized_8l_percent": 87.50983221571457
-      },
-      {
-        "layers": 4,
-        "metric": "peak_rss_mib",
-        "metric_label": "Peak RSS",
-        "model": "4L proxy",
-        "normalized_8l_percent": 78.19200407435702
-      },
-      {
-        "layers": 8,
-        "metric": "mean_core_seconds_per_round",
-        "metric_label": "Core-seconds",
-        "model": "8L",
-        "normalized_8l_percent": 100.0
-      },
-      {
-        "layers": 6,
-        "metric": "mean_core_seconds_per_round",
-        "metric_label": "Core-seconds",
-        "model": "6L",
-        "normalized_8l_percent": 76.16702179915283
-      },
-      {
-        "layers": 4,
-        "metric": "mean_core_seconds_per_round",
-        "metric_label": "Core-seconds",
-        "model": "4L proxy",
-        "normalized_8l_percent": 51.03561371028788
-      }
-    ]
-  }
-}

benchmark/figures/fig10.py ADDED Viewed

	@@ -0,0 +1,92 @@

+"""Figure 10: the published root against every arm measured under the frozen protocol, on every metric."""
+import csv, json, pathlib
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+S = pathlib.Path("/tmp/claude-1000/-home-mp-ubuntu-Projects-paper-qwen-intent-classifier/"
+                 "0af32b5a-ac18-4e73-a3ad-1c863d0154af/scratchpad")
+OUT = S / "fig2"
+SURFACE = "#fcfcfb"; INK, INK2, MUTED = "#1a1a19", "#4a4a47", "#8a8a85"; GRID = "#e5e5e1"
+C = {"blue": "#2a78d6", "orange": "#eb6834", "aqua": "#1baf7a", "yellow": "#eda100", "violet": "#4a3aa7"}
+plt.rcParams.update({
+    "figure.facecolor": SURFACE, "axes.facecolor": SURFACE, "savefig.facecolor": SURFACE,
+    "axes.edgecolor": GRID, "axes.linewidth": 0.8, "axes.labelcolor": INK2,
+    "xtick.color": MUTED, "ytick.color": MUTED, "text.color": INK,
+    "font.size": 9, "axes.titlesize": 10.5, "axes.titleweight": "semibold",
+    "grid.color": GRID, "grid.linewidth": 0.7,
+    "xtick.major.size": 0, "ytick.major.size": 0,
+    "legend.frameon": False, "figure.dpi": 160,
+})
+arms = json.loads((S / "merged_arms.json").read_text())
+MI = 1024 ** 2
+rows = {r["arm_id"]: r for r in csv.DictReader(open(str(S / "hf/bench/61_arm_bf16.csv")))}
+g = json.loads(pathlib.Path("/home/mp_ubuntu/Projects/paper_qwen_intent_classifier/agents/sessions/"
+                            "2026-09-20/S-20260920-taskblind-ladder-v1/MEASUREMENTS.json").read_text())
+gridN = {"15,380": "8192", "23,551": "16384", "39,866": "32768", "72,455": "65536"}
+for a in arms:
+    if a["new"]:
+        n = next(v for k, v in gridN.items() if k in a["arm_id"])
+        a["peak_rss_mib"] = g["arms"][n]["BF16"]["peak_tree_rss_bytes"] / MI
+    else:
+        a["peak_rss_mib"] = float(rows[a["arm_id"]]["peak_rss_mib"])
+FINAL = "39,866"
+def fam(a):
+    if a["new"]: return "final" if FINAL in a["arm_id"] else "grid"
+    return "qwen" if a["arm_id"].startswith(("qwen35", "qwen25")) else "encoder"
+COL = {"encoder": MUTED, "qwen": C["violet"], "grid": C["blue"], "final": C["orange"]}
+PANELS = [
+    ("macro_f1_mean", "macro F1  —  higher is better", True, False, "{:.4f}"),
+    ("p50_ms", "document p50, ms  —  lower is better", False, True, "{:.1f}"),
+    ("peak_rss_mib", "peak process-tree RSS, MiB  —  lower is better", False, False, "{:.0f}"),
+    ("weight_file_mib", "BF16 weight file, MiB  —  lower is better", False, True, "{:.0f}"),
+    ("gpu_j_per_doc", "GPU joules per document  —  lower is better", False, True, "{:.2f}"),
+]
+fig, axes = plt.subplots(len(PANELS), 1, figsize=(11.0, 7.4))
+for ax, (key, title, higher, logx, fmt) in zip(axes, PANELS):
+    ax.set_axisbelow(True)
+    ax.grid(True, axis="x", alpha=0.9)
+    for s in ("top", "right", "left"): ax.spines[s].set_visible(False)
+    ax.spines["bottom"].set_color(GRID)
+    vals = [a[key] for a in arms]
+    root = next(a for a in arms if fam(a) == "final")
+    order = sorted(vals, reverse=higher)
+    rank = order.index(root[key]) + 1
+    for a in arms:
+        f = fam(a)
+        if f == "final": continue
+        ax.plot([a[key], a[key]], [0.18, 0.82], lw=1.6 if f == "grid" else 1.1,
+                color=COL[f], alpha=0.95 if f == "grid" else 0.45, solid_capstyle="butt", zorder=3)
+    ax.plot([root[key]], [0.5], marker="*", ms=17, color=C["orange"],
+            markeredgecolor=SURFACE, markeredgewidth=1.2, zorder=6)
+    if logx: ax.set_xscale("log")
+    ax.set_yticks([]); ax.set_ylim(0, 1.25)
+    ax.set_title(title, loc="left", pad=6)
+    good = "top" if rank <= len(arms) / 3 else ("middle" if rank <= 2 * len(arms) / 3 else "bottom")
+    ax.annotate(f"this root {fmt.format(root[key])}  ·  rank {rank} of {len(arms)}  ({good} third)",
+                (0.998, 1.06), xycoords="axes fraction", ha="right", va="bottom",
+                fontsize=8.5, color=C["orange"], fontweight="semibold")
+    lo, hi = min(vals), max(vals)
+    ax.annotate(f"best {fmt.format(order[0])}", (hi if higher else lo, 0.02),
+                xycoords=("data", "axes fraction"), ha="right" if higher else "left",
+                va="bottom", fontsize=7, color=MUTED)
+handles = [plt.Line2D([], [], color=COL["encoder"], lw=2, label="encoder families (46)"),
+           plt.Line2D([], [], color=COL["qwen"], lw=2, label="earlier Qwen arms (15)"),
+           plt.Line2D([], [], color=COL["grid"], lw=2, label="task-blind vocabulary grid (4)"),
+           plt.Line2D([], [], color=C["orange"], marker="*", ms=13, lw=0, label="this root: 4L, 39,866 EN/KO")]
+fig.legend(handles=handles, loc="lower center", ncol=4, fontsize=8.5, bbox_to_anchor=(0.5, 0.062))
+fig.suptitle("The published root against all 65 arms, on every metric the protocol measured",
+             fontsize=12, fontweight="semibold", y=0.988)
+fig.text(0.5, 0.030, "Each tick is one arm. Quality is a transfer probe with a fresh 14-label head, three seeds; the other four are single measured values.",
+         ha="center", fontsize=7.5, color=MUTED)
+fig.text(0.5, 0.008, "The four grid arms were measured in a later session; a same-host check on an earlier cut put the host effect at 1.05x on BF16 latency and 0.99x on RSS, so read small latency gaps with that in mind.",
+         ha="center", fontsize=7.5, color=MUTED)
+fig.subplots_adjust(left=0.035, right=0.985, top=0.925, bottom=0.135, hspace=0.72)
+fig.savefig(OUT / "10_root_on_every_metric.png")
+print("wrote 10_root_on_every_metric.png")

benchmark/figures/manifest.json DELETED Viewed

@@ -1,14 +0,0 @@
-{
-  "schema_version": "standalone4l-v128k-figures-v1",
-  "quality_scope": "existing opened SemEval transfer evidence; 128k vs 248k is not causal because lineages differ",
-  "source_files": [
-    "results/standalone4l_base/20260731-v1/huggingface_release_v3/benchmark/arm_summary.csv",
-    "results/standalone4l_base/20260731-v1/huggingface_release_v3/benchmark/compression_ladder.csv",
-    "results/standalone4l_base/20260731-v1/huggingface_release_v3/benchmark/resource_metrics.csv"
-  ],
-  "figures": [
-    "v128k_vocabulary_size.png",
-    "v128k_quality_storage.png"
-  ],
-  "new_headless_quality": "NR: no new downstream benchmark run"
-}

benchmark/figures/source_summary.json DELETED Viewed

@@ -1,891 +0,0 @@
-{
-  "schema": "standalone4l-complete-transfer-benchmark-v2",
-  "status": "complete_from_frozen_reports",
-  "claim_scope": "Exploratory fresh-head transfer evidence on one English task and a previously opened 55-article SemEval-derived test split.",
-  "headless_root_requires_adaptation": true,
-  "test_previously_opened": true,
-  "test_articles": 55,
-  "test_windows": 434,
-  "seeds": [
-    41,
-    42,
-    43
-  ],
-  "three_seed_sd_is_not_confidence_interval": true,
-  "multilingual_downstream_evaluation_performed": false,
-  "upstream_multilingual_metadata": {
-    "source": "Qwen/Qwen3.5-0.8B official model card",
-    "languages_and_dialects_claimed_upstream": 201,
-    "scope": "inherited tokenizer and architecture coverage only"
-  },
-  "protocol": {
-    "epochs": 5,
-    "primary_epoch": 5,
-    "early_stopping": false,
-    "max_length": 256,
-    "stride": 128,
-    "window_target": "span-aligned technique labels",
-    "article_aggregation": "labelwise maximum probability",
-    "micro_batch_size": 1,
-    "gradient_accumulation": 32,
-    "effective_batch_size": 32,
-    "learning_rate": 2e-05,
-    "weight_decay": 0.01,
-    "gradient_clip": 1.0,
-    "optimizer": "AdamW",
-    "dtype": "bfloat16",
-    "thresholds": [
-      0.05,
-      0.1,
-      0.15,
-      0.2,
-      0.25,
-      0.3,
-      0.35,
-      0.4,
-      0.45,
-      0.5,
-      0.55,
-      0.6,
-      0.65,
-      0.7,
-      0.75,
-      0.8,
-      0.85,
-      0.9
-    ],
-    "threshold_selection": "epoch-5 calibration macro-F1, then micro-F1"
-  },
-  "arms": {
-    "task_agnostic_base": {
-      "display": "Task-agnostic KD base",
-      "lineage": "root base plus a fresh 14-label transfer head",
-      "metrics": {
-        "macro_f1": {
-          "values": [
-            0.5883093293517536,
-            0.6028379036218096,
-            0.5712816197954101
-          ],
-          "mean": 0.5874762842563245,
-          "sample_sd": 0.01579462678670608,
-          "n": 3
-        },
-        "micro_f1": {
-          "values": [
-            0.6468085106382978,
-            0.6655172413793103,
-            0.6465256797583082
-          ],
-          "mean": 0.6529504772586388,
-          "sample_sd": 0.010884055708931178,
-          "n": 3
-        },
-        "exact_match": {
-          "values": [
-            0.05454545454545454,
-            0.03636363636363636,
-            0.0
-          ],
-          "mean": 0.0303030303030303,
-          "sample_sd": 0.027773186030035393,
-          "n": 3
-        },
-        "predicted_positive_rate": {
-          "values": [
-            0.5714285714285714,
-            0.4090909090909091,
-            0.5155844155844156
-          ],
-          "mean": 0.4987012987012987,
-          "sample_sd": 0.08247520160679181,
-          "n": 3
-        }
-      },
-      "resources": {
-        "parameters": {
-          "values": [
-            334111040.0,
-            334111040.0,
-            334111040.0
-          ],
-          "mean": 334111040.0,
-          "sample_sd": 0.0,
-          "n": 3
-        },
-        "peak_cuda_allocated_bytes": {
-          "values": [
-            3380975616.0,
-            3380975616.0,
-            3380975616.0
-          ],
-          "mean": 3380975616.0,
-          "sample_sd": 0.0,
-          "n": 3
-        },
-        "peak_cuda_reserved_bytes": {
-          "values": [
-            3519021056.0,
-            3519021056.0,
-            3510632448.0
-          ],
-          "mean": 3516224853.3333335,
-          "sample_sd": 4843165.086926248,
-          "n": 3
-        },
-        "total_wall_seconds": {
-          "values": [
-            1583.0297977919981,
-            1588.617994428001,
-            1603.4310500120046
-          ],
-          "mean": 1591.692947410668,
-          "sample_sd": 10.54249851412311,
-          "n": 3
-        },
-        "test_seconds_per_article": {
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-            0.08164824154545468,
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-          ],
-          "mean": 0.08130633847886752,
-          "sample_sd": 0.0024347419754560395,
-          "n": 3
-        }
-      },
-      "thresholds": [
-        0.15,
-        0.3,
-        0.1
-      ]
-    },
-    "structural_copy_control": {
-      "display": "Structural copy, no task-free KD",
-      "lineage": "same four-layer structure without task-free KD",
-      "metrics": {
-        "macro_f1": {
-          "values": [
-            0.5169424579262757,
-            0.5349405779540175,
-            0.5023778364496152
-          ],
-          "mean": 0.5180869574433028,
-          "sample_sd": 0.016311512588628545,
-          "n": 3
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-        "micro_f1": {
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-          ],
-          "mean": 0.5904584952122559,
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-          "n": 3
-        },
-        "exact_match": {
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-          "mean": 0.0,
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-        },
-        "predicted_positive_rate": {
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-          ],
-          "mean": 0.6432900432900432,
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-          "n": 3
-        }
-      },
-      "resources": {
-        "parameters": {
-          "values": [
-            334111040.0,
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-          ],
-          "mean": 334111040.0,
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-        "peak_cuda_reserved_bytes": {
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-            3519021056.0,
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-          "sample_sd": 4843165.086926248,
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-        },
-        "total_wall_seconds": {
-          "values": [
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-          ],
-          "mean": 1609.4498868319981,
-          "sample_sd": 29.457874198835384,
-          "n": 3
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-        "test_seconds_per_article": {
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-          ],
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training_manifest.json CHANGED Viewed

@@ -3,11 +3,11 @@
   "status": "completed",
   "timestamp_kst": "2026-09-20T07:56:37.853994+09:00",
   "stage": "6to4",
-  "teacher": "/home/mp_ubuntu/Projects/paper_qwen_intent_classifier/results/standalone4l_base/20260920-taskblind-grid/N32768/base-6l",
-  "student_initial": "/home/mp_ubuntu/Projects/paper_qwen_intent_classifier/results/standalone4l_base/20260920-taskblind-grid/N32768/init-4l",
-  "output": "/home/mp_ubuntu/Projects/paper_qwen_intent_classifier/results/standalone4l_base/20260920-taskblind-grid/N32768/base-4l",
   "data": {
-    "path": "/home/mp_ubuntu/Projects/paper_qwen_intent_classifier/data/public_corpora/wikitext-103-raw-v1-b08601e04326/train-00000-of-00002.parquet",
     "sha256": "74da360f23826045b3e6ac6375411fdb15f003030aa74f2596ed08b857cb9212",
     "rows_used": 4096,
     "labels_read": false,

   "status": "completed",
   "timestamp_kst": "2026-09-20T07:56:37.853994+09:00",
   "stage": "6to4",
+  "teacher": "results/standalone4l_base/20260920-taskblind-grid/N32768/base-6l",
+  "student_initial": "results/standalone4l_base/20260920-taskblind-grid/N32768/init-4l",
+  "output": "results/standalone4l_base/20260920-taskblind-grid/N32768/base-4l",
   "data": {
+    "path": "data/public_corpora/wikitext-103-raw-v1-b08601e04326/train-00000-of-00002.parquet",
     "sha256": "74da360f23826045b3e6ac6375411fdb15f003030aa74f2596ed08b857cb9212",
     "rows_used": 4096,
     "labels_read": false,