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junshengma
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Model Details
Model Provider
junshengma
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| 指标 | 基座 | 本适配器 |
|---|---|---|
| 综合质量分 | 2.44 | 2.47 |
| 论据充分度 | 1.38 | 1.62 |
| 无版式垃圾 | 5.00 | 4.81 |
| 平均正文字数 | 1752 | 2878 |
数据消融实验表明：清洗训练数据（版式噪声 67%→4.5%）后，模型综合分从低于基座（2.09）回升并反超（2.47）。
python
import torchfrom peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigBASE = "Qwen/Qwen2.5-7B-Instruct"ADAPTER = "junshengma/qwen2.5-7b-gzh-writing-qlora"bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True,)tok = AutoTokenizer.from_pretrained(BASE)model = AutoModelForCausalLM.from_pretrained(BASE, quantization_config=bnb, device_map="cuda:0")model = PeftModel.from_pretrained(model, ADAPTER)messages = [{"role": "system", "content": "你是一位科技领域的深度评论员。请先分析技术趋势背后的产业逻辑与争议点，再撰写一篇有论据、有观点、不蹭热度的科技评论文章。"},{"role": "user", "content": "主题：AI Agent 会取代 App 吗\n受众：关注科技的互联网从业者\n要求：先输出 <thinking> 再输出 <article>，观点鲜明、有论据"},]text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tok(text, return_tensors="pt").to("cuda")out = model.generate(**inputs, max_new_tokens=3072, temperature=0.7, top_p=0.9, repetition_penalty=1.05, do_sample=True)print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
输出格式为 <thinking>规划式思考链</thinking> + <article>正文</article>，推理时可仅取 <article> 部分。
| 参数 | 值 |
|---|---|
| lora_rank / alpha | 16 / 32 |
| learning_rate | 1e-4 (cosine) |
| epochs | 3 |
| cutoff_len | 6144 |
| batch × grad_accum | 1 × 8 |
| optimizer | paged_adamw_8bit |
完整训练/数据管线见 GitHub 仓库：https://github.com/JUNSHENG428/gzh-deep-writing-qlora