linlu-lora-v0.2-qwen3.6-27b

Lin Lu (林路) persona LoRA — v0.2

⚠️ Superseded. Use linlu-lora-v0.4-qwen3.6-27b — same 27B base, 2.3× larger corpus, plus timed [HH:MM] pacing and [[sticker:ref]] sticker output (eval loss 2.802 → 1.769).

Adapter for the 林路 character — a ~30 y.o. Tang/Song-poetry literature lecturer, trained from the Divergence 2% writer corpus (89 user-days, 5,462 assistant turns, ~216K supervised tokens).

Behavior: short WeChat-style multi-bubble replies (one line = one bubble), Chinese inner monologue in <think>…</think>, canon-consistent refusals (won't do mental math, deflects jailbreaks in character).

Older ablation: linlu-lora-v0.1-qwen3.6-35b-a3b.

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Requirements

pip install "transformers>=5.5" peft torch accelerate

You need a GPU with ~55 GB VRAM for bf16 inference (27B base + LoRA). trust_remote_code=True is required for Qwen3.6.

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Quick start (copy-paste runnable)

import json
import re
import torch
import transformers
from peft import PeftModel
from transformers import AutoTokenizer

BASE = "Qwen/Qwen3.6-27B"
ADAPTER = "s-g-labs/linlu-lora-v0.2-qwen3.6-27b"

# 1. Load — use the exact architecture class from config, NOT AutoModelForCausalLM
from transformers import AutoConfig
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()

# Sanity check: adapter must actually be attached (silent failure otherwise)
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, f"adapter not loaded ({n} LoRA modules)"
print(f"OK — {n} LoRA modules active")

# 2. Persona system prompt (customize user_name, intimacy, relationship, etc.)
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。
请始终以林路的身份、语气和价值观说话，保持人物一致性，像真人发微信一样自然、口语化，
可以一次发多条短消息。不要解释你是AI，不要跳出角色。

【人物能力与爱好】
擅长领域：文学、网球、羽毛球、电吉他、Steam游戏
不擅长的事：数学、手工活、做饭（黑暗料理）

【价值观（按重要性排序）】
理想、理性、正义、自由、尊重

【人生经历】
文学博士，大学讲师，主讲唐宋文学史。

【与对方的关系】
两人刚认识，还在互相了解。

【当前亲密度】15（满分100，数值越高关系越亲近）
"""

# 3. ChatML prompt (template: qwen / ChatML)
def chatml(system: str, history: list[tuple[str, str]]) -> str:
    s = f"<|im_start|>system\n{system}<|im_end|>\n"
    for role, content in history:
        s += f"<|im_start|>{role}\n{content}<|im_end|>\n"
    return s + "<|im_start|>assistant\n"

history = [("user", "周末一般干嘛？")]
prompt = chatml(system, history)
ids = tok(prompt, return_tensors="pt").to(model.device)
eos = [tok.eos_token_id, tok.convert_tokens_to_ids("<|im_end|>")]

with torch.no_grad():
    out = model.generate(
        **ids,
        max_new_tokens=300,
        do_sample=True,
        temperature=0.8,
        top_p=0.95,
        top_k=20,
        repetition_penalty=1.05,
        no_repeat_ngram_size=8,
        eos_token_id=eos,
        pad_token_id=tok.pad_token_id or eos[0],
    )
raw = tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True)

# 4. Split inner monologue vs visible chat bubbles
m = re.search(r"<think>(.*?)</think>(.*)", raw, re.S)
think = m.group(1).strip() if m else ""
reply = (m.group(2) if m else raw).strip()
bubbles = [ln.strip() for ln in reply.split("\n") if ln.strip()]

print("内心独白:", think or "(empty)")
print("林路 bubbles:", bubbles)

Expected output shape (not verbatim):

内心独白: 她问我周末干嘛，想找个话题聊聊……
林路 bubbles: ['打羽毛球啊，打网球啊', '还有看电影打游戏', '你呢？']

Each string in bubbles is one WeChat message bubble. Render them separately in your UI.

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Prompt format

The model was trained to emit:

<think>
{Chinese inner monologue — optional, may be empty}
</think>

{bubble 1}
{bubble 2}
…

Strip [客观] / [主观] / [回复] if they ever appear (training-data artifact; rare in v0.2).

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Recommended sampling

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Multi-turn chat

Keep prior turns in history as alternating user / assistant pairs. Store the raw assistant text (including <think>) in history — that is what the model saw during training.

history = [
    ("user", "在吗"),
    ("assistant", "<think>\n对方打招呼了\n</think>\n\n在\n有什么事"),
    ("user", "今天好累"),
]
# then chatml(system, history) → generate → append new assistant turn

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Cold open (林路 speaks first)

Send this as the user turn when you want Lin Lu to proactively open a new day:

（系统提示：新的一天开始了，请像往常一样主动给对方发消息，开启今天的聊天。）

Half of the training days start this way; the model learns to greet first without waiting for a user message.

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Persona fields

The system prompt is built from a character card. Important fields:

Change these per session to simulate different users or relationship stages.

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LLaMA-Factory chat CLI

If you have the base model locally and LLaMA-Factory installed:

llamafactory-cli chat \
  --model_name_or_path ./Qwen3.6-27B \
  --adapter_name_or_path s-g-labs/linlu-lora-v0.2-qwen3.6-27b \
  --template qwen \
  --trust_remote_code

Paste the persona system prompt when prompted.

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Common mistakes

1. AutoModelForCausalLM — resolves to a different module tree; PEFT attaches the adapter to nothing and you get the untrained base. Use architectures[0] from config.json (Qwen3_5ForConditionalGeneration for this base).

2. Merging one bubble — the model uses newlines deliberately. Split on \n after stripping the think block.

3. Dropping <think> from history — include the full assistant turn in multi-turn context.

4. v0.1 for production — use this repo (v0.2). v0.1 does math out of character and leaks writer scaffolding.

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Training config

See training_config.yaml in this repo. Data prep: chat JSONL with {"messages": [{"role","content"}, …]} (ShareGPT format), assistant turns prefixed with <think>…</think>\n\n.