s-g-labs/linlu-lora-v0.4-qwen3.6-27b

Lin Lu (林路) persona LoRA — v0.4

Recommended production adapter. Combines the v0.2 base model (Qwen3.6-27B, best conversational depth) with the v0.3 data pipeline (timestamped bubbles + sticker catalog refs, 204 user-days / 8,559 assistant turns from the Divergence 2% production corpus).

Base	Qwen/Qwen3.6-27B (dense)
LoRA rank / α	32 / 64
Targets	q/k/v/o_proj + MLP (gate/up/down_proj)
Eval loss	1.769 (5 epochs; vs 1.869 for v0.3-9B, 2.802 for v0.2)

Lineage: v0.2 = 27B + old flattened data · v0.3 = 9B + v3 data · v0.4 = 27B + v3 data

Measured on an 8-scenario probe suite (cold open, pragmatic traps, hostile users, jailbreak, emotional support, callbacks): 100% well-formed [HH:MM] stamps, in-character jailbreak deflection, natural sticker usage, ~3x more concise than v0.3.

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Output format

<think>
{Chinese inner monologue — optional}
</think>
[19:05] 晚上好呀
[19:06] [[sticker:挠头]]
[19:06] 在忙吗？

Each line is one WeChat-style bubble. [HH:MM] stamps encode the reply pacing (story-world clock — use deltas to drive typing delays, display wall-clock time). Resolve [[sticker:ref]] against your sticker catalog.

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Quick start

import torch
import transformers
from peft import PeftModel
from transformers import AutoTokenizer, AutoConfig

BASE = "Qwen/Qwen3.6-27B"
ADAPTER = "s-g-labs/linlu-lora-v0.4-qwen3.6-27b"

# Use the exact architecture class — AutoModelForCausalLM resolves to a
# mismatched module tree and PEFT silently attaches nothing.
arch = AutoConfig.from_pretrained(BASE, trust_remote_code=True).architectures[0]
cls = getattr(transformers, arch)
tok = AutoTokenizer.from_pretrained(BASE, trust_remote_code=True)
model = cls.from_pretrained(BASE, dtype=torch.bfloat16, device_map="auto",
                            trust_remote_code=True)
model = PeftModel.from_pretrained(model, ADAPTER)
model.eval()

n = sum(1 for k, m in model.named_modules()
        if k.endswith("lora_B.default") and m.weight.abs().max() > 0)
assert n > 0, "adapter not loaded"

system = """你将扮演「林路」，一个真实、有血有肉的角色，正在用中文和「对方」聊天。
【消息格式】每条消息单独一行，以发送时刻开头，格式为「[HH:MM] 消息内容」。
发表情包时整条消息写作「[HH:MM] [[sticker:表情名]]」。"""

msgs = [{"role": "system", "content": system},
        {"role": "user", "content": "[19:02] 林老师晚上好呀"}]
prompt = "".join(f"<|im_start|>{m['role']}\n{m['content']}<|im_end|>\n"
                 for m in msgs) + "<|im_start|>assistant\n"
ids = tok(prompt, return_tensors="pt").to(model.device)
eos = [tok.eos_token_id, tok.convert_tokens_to_ids("<|im_end|>")]
out = model.generate(**ids, max_new_tokens=512, do_sample=True,
                     temperature=0.7, top_p=0.95, top_k=20,
                     eos_token_id=eos, pad_token_id=tok.pad_token_id or eos[0])
print(tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True))

Requires ~55 GB VRAM (bf16). For an OpenAI-compatible server, use the serve/ bundle from the v0.3 repo — identical protocol, just pass --base Qwen/Qwen3.6-27B --adapter <this repo>.

Sampling — IMPORTANT

Never use no_repeat_ngram_size — the timed format legitimately repeats [HH:MM] prefixes, and n-gram blocking forces the model into corrupted stamps ([18:48h], stray ] lines). A moderate repetition_penalty is fine and recommended: at 1.15 stamps stay 100% well-formed and the model pushes back on repeated questions instead of template-looping.

Parameter	Value
temperature	0.7
top_p	0.95
top_k	20
repetition_penalty	1.15 (keep ≤ 1.3)
max_new_tokens	512–1024

Conversation-history contract — IMPORTANT

Long-chat quality depends on feeding history back in the exact training shape. Consecutive same-role messages were merged into one turn during training (bubbles joined by \n), and every assistant turn starts with its <think>…</think> block (empty block if none). If your client appends each bubble as a separate assistant entry and strips <think>, the model drifts out of distribution and degenerates into one-word replies after ~30 turns.

user:      [19:02] 在吗\n[19:02] 林老师
assistant: <think>\n{monologue}\n</think>\n[19:05] 在的\n[19:05] 怎么了

Known limitations

• Canon slip: unlike v0.2, v0.4 sometimes answers mental math correctly (persona says Lin Lu is bad at math). The 27B's ability leaks through.
• Sticker refs are usually valid catalog entries but occasionally invented — render a text fallback for unknown refs.