pplboy
/

test

@@ -2,205 +2,180 @@
 base_model: Qwen/Qwen2.5-0.5B-Instruct
 library_name: peft
 tags:
-- base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct
 - lora
-- transformers
 ---
-# Model Card for Model ID
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-## Model Details
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-### Framework versions
-- PEFT 0.16.0

 base_model: Qwen/Qwen2.5-0.5B-Instruct
 library_name: peft
 tags:
 - lora
+- qwen
+- customer-service
+- chinese
+- conversational
+license: mit
+language:
+- zh
 ---
+# Qwen2.5-0.5B-Instruct 客服微调模型
+这是一个基于 Qwen2.5-0.5B-Instruct 使用 LoRA 方法微调的客服对话模型。
+## 模型详情
+- **基础模型**: [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
+- **微调方法**: LoRA (Low-Rank Adaptation)
+- **参数量**: 基础模型 0.5B + LoRA 适配器 ~2MB
+- **语言**: 中文
+- **用途**: 客服对话、智能问答
+## 使用方法
+### 安装依赖
+```bash
+pip install transformers peft torch
+```
+### 加载和使用模型
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+# 加载基础模型
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2.5-0.5B-Instruct",
+    torch_dtype="auto",
+    device_map="auto"
+)
+# 加载 LoRA 适配器
+model = PeftModel.from_pretrained(
+    base_model,
+    "pplboy/test"
+)
+# 加载分词器
+tokenizer = AutoTokenizer.from_pretrained("pplboy/test")
+# 使用模型
+prompt = "你好，我想咨询一下产品"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=100,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True
+)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+### 使用 Transformers Pipeline
+```python
+from transformers import pipeline
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+# 加载模型
+config = PeftConfig.from_pretrained("pplboy/test")
+base_model = AutoModelForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
+    torch_dtype="auto",
+    device_map="auto"
+)
+model = PeftModel.from_pretrained(base_model, "pplboy/test")
+tokenizer = AutoTokenizer.from_pretrained("pplboy/test")
+# 创建 pipeline
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    device_map="auto"
+)
+# 生成回复
+result = pipe("你好，我想咨询一下产品", max_new_tokens=100)
+print(result[0]['generated_text'])
+```
+## 模型信息
+### LoRA 配置
+- **LoRA rank (r)**: 8
+- **LoRA alpha**: 16
+- **LoRA dropout**: 0.1
+- **Target modules**: q_proj, v_proj
+### 训练信息
+- **训练框架**: PEFT 0.16.0
+- **训练方法**: LoRA 微调
+- **基础模型**: Qwen2.5-0.5B-Instruct
+## 使用场景
+- 智能客服系统
+- 自动问答
+- 对话机器人
+- 客户支持
+## 限制和注意事项
+1. **需要基础模型**: 这是一个 LoRA 适配器，使用前需要先加载基础模型 `Qwen/Qwen2.5-0.5B-Instruct`
+2. **模型大小**: 基础模型约 1GB，LoRA 适配器约 2MB
+3. **内存要求**: 建议至少 4GB 内存（使用 GPU 可减少内存占用）
+4. **语言支持**: 主要支持中文，英文能力有限
+## 示例
+```python
+# 完整示例
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+# 加载
+base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
+model = PeftModel.from_pretrained(base_model, "pplboy/test")
+tokenizer = AutoTokenizer.from_pretrained("pplboy/test")
+# 测试对话
+conversations = [
+    "你好，我想咨询一下产品",
+    "这个产品有什么特点？",
+    "如何退货？",
+    "客服工作时间是什么时候？"
+]
+for prompt in conversations:
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.7)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    print(f"Q: {prompt}")
+    print(f"A: {response}\n")
+```
+## 引用
+如果使用本模型，请引用：
+```bibtex
+@misc{pplboy-test-2024,
+  title={Qwen2.5-0.5B-Instruct 客服微调模型},
+  author={pplboy},
+  year={2024},
+  howpublished={\url{https://huggingface.co/pplboy/test}}
+}
+```
+## 许可证
+本模型基于 Qwen2.5-0.5B-Instruct，遵循 MIT 许可证。
+## 相关链接
+- [基础模型](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
+- [PEFT 文档](https://huggingface.co/docs/peft)
+- [Transformers 文档](https://huggingface.co/docs/transformers)