Update README.md

add english model card

README.md

@@ -10,25 +10,27 @@ tags:
 pipeline_tag: text-generation
 ---
 
-# Qwen-7B LoRA 微调模型（中文指令微调）
+# 📌 中文简介：Qwen-7B LoRA 微调模型（中文指令微调）
 
-本模型基于阿里巴巴通义千问 Qwen-7B-Chat，采用 LoRA 技术，使用 Alpaca-Zh-51k 数据集进行中文指令微调。适合中文指令理解与生成任务。（错误示范，用base模型微调，别用chat /(ㄒoㄒ)/~~）
+本模型基于阿里巴巴通义千问 Qwen-7B-Chat，采用 LoRA 技术，使用 Alpaca-Zh-51k 数据集进行了中文指令微调，适用于中文任务的理解与生成。
 
-## 训练说明
+注： 对Chat进行微调后效果反而变差了，或许对base版本微调会好一些
 
-- **基座模型**：Qwen-7B-Chat（未上传原始权重，仅上传 LoRA adapter）
-- **微调方法**：LoRA
-- **数据集**：Alpaca-Zh-51k
-- **训练脚本**：见仓库 `train_qwen7b_lora.py`
-- **推理/对比脚本**：见仓库 `test_compare.py`
+## 🧾 模型信息
 
-## 使用方法
+- **基座模型**：[`Qwen/Qwen-7B-Chat`](https://huggingface.co/Qwen/Qwen-7B-Chat)
+- **微调方法**：LoRA（使用 PEFT 库）
+- **训练数据集**：Alpaca-Zh-51k
+- **训练脚本**：`train_qwen7b_lora.py`
+- **推理脚本**：`test_compare.py`
+- ⚠️ 本模型仅包含 LoRA adapter，不包含原始基座权重
+
+## 🚀 使用示例
 
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 from peft import PeftModel
 
-# 替换为你的 Hugging Face 用户名和模型名
 model_name = "Josh1207/qwen7b-alpaca-lora"
 
 tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
@@ -38,4 +40,42 @@ model = PeftModel.from_pretrained(base_model, model_name)
 prompt = "指令: 请介绍一下你自己。"
 inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
 outputs = model.generate(**inputs, max_new_tokens=512)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+````
+
+
+---
+
+# 📌 English Overview: Qwen-7B LoRA Fine-tuned Model (Chinese Instruction Tuning)
+
+This model is fine-tuned from Alibaba’s Qwen-7B-Chat using LoRA technique on the Alpaca-Zh-51k dataset. It is suitable for instruction-following tasks in Chinese.
+
+(I found that after making adjustments to Chat model, the effect actually got worse. Perhaps making adjustments to the base version would be better)
+
+## 🧾 Model Information
+
+* **Base model**: [`Qwen/Qwen-7B-Chat`](https://huggingface.co/Qwen/Qwen-7B-Chat)
+* **Tuning method**: LoRA (via `peft`)
+* **Dataset**: Alpaca-Zh-51k
+* **Training script**: `train_qwen7b_lora.py`
+* **Inference script**: `test_compare.py`
+* ⚠️ This repository includes only LoRA adapter weights, not the original base model.
+
+## 🚀 Usage Example
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+model_name = "Josh1207/qwen7b-alpaca-lora"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", trust_remote_code=True)
+model = PeftModel.from_pretrained(base_model, model_name)
+
+prompt = "指令: 请介绍一下你自己。"
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+