中文 ｜ English
## 介绍
**Llama3-Chinese**是**以Meta-Llama-3-8B为底座**，使用 [DORA](https://arxiv.org/pdf/2402.09353.pdf) + [LORA+](https://arxiv.org/pdf/2402.12354.pdf) 的训练方法，在50w高质量中文多轮SFT数据 + 10w英文多轮SFT数据 + 2000单轮自我认知数据训练而来的大模型。
**Github:** [https://github.com/seanzhang-zhichen/llama3-chinese](https://github.com/seanzhang-zhichen/llama3-chinese)
## 模型下载
| Model | Download |
|:-------------------:|:-----------:|
| Meta-Llama-3-8B |[ 🤗 HuggingFace](https://huggingface.co./meta-llama/Meta-Llama-3-8B) [ 🤖 ModelScope](https://modelscope.cn/models/LLM-Research/Meta-Llama-3-8B)|
| Llama3-Chinese-Lora |[ 🤗 HuggingFace](https://huggingface.co./zhichen/Llama3-Chinese-Lora) [ 🤖 ModelScope](https://modelscope.cn/models/seanzhang/Llama3-Chinese-Lora)|
| Llama3-Chinese (合并好的模型) |[ 🤗 HuggingFace](https://huggingface.co./zhichen/Llama3-Chinese) [ 🤖 ModelScope](https://modelscope.cn/models/seanzhang/Llama3-Chinese)|
## 合并LORA模型（可跳过）
1、下载 [Meta-Llama-3-8B](https://modelscope.cn/models/LLM-Research/Meta-Llama-3-8B)
```bash
git clone https://www.modelscope.cn/LLM-Research/Meta-Llama-3-8B.git
```
2、下载[Llama3-Chinese-Lora](https://www.modelscope.cn/models/seanzhang/Llama3-Chinese-Lora)
**From ModelScope**
```bash
git lfs install
git clone https://www.modelscope.cn/seanzhang/Llama3-Chinese-Lora.git
```
**From HuggingFace**
```bash
git lfs install
git clone https://huggingface.co./zhichen/Llama3-Chinese-Lora
```
3、合并模型
```bash
python merge_lora.py \
--base_model path/to/Meta-Llama-3-8B \
--lora_model path/to/lora/Llama3-Chinese-Lora \
--output_dir ./Llama3-Chinese
```
## 下载 Llama3-Chinese（合并好的模型）
**From ModelScope**
```bash
git lfs install
git clone https://www.modelscope.cn/seanzhang/Llama3-Chinese.git
```
**From HuggingFace**
```bash
git lfs install
git clone https://huggingface.co./zhichen/Llama3-Chinese
```
## 推理
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "zhichen/Llama3-Chinese"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "你好"},
]
input_ids = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
input_ids,
max_new_tokens=2048,
do_sample=True,
temperature=0.7,
top_p=0.95,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
## 命令行推理
```bash
python cli_demo.py --model_path zhichen/Llama3-Chinese
```
## web推理
```bash
python web_demo.py --model_path zhichen/Llama3-Chinese
```
## vllm web 推理
1、使用[vllm](https://github.com/vllm-project/vllm)部署模型
```bash
python -m vllm.entrypoints.openai.api_server --served-model-name Llama3-Chinese --model ./Llama3-Chinese(换成你自己的合并后的模型路径)
```
2、在命令行执行
```bash
python vllm_web_demo.py --model Llama3-Chinese
```
## 训练数据集
[匠数科技大模型sft数据集](https://modelscope.cn/datasets/deepctrl/deepctrl-sft-data)
## LICENSE
本项目仅可应用于研究目的，项目开发者不承担任何因使用本项目（包含但不限于数据、模型、代码等）导致的危害或损失。详细请参考[免责声明](https://github.com/seanzhang-zhichen/Llama3-Chinese/blob/main/DISCLAIMER)。
Llama3-Chinese项目代码的授权协议为 [The Apache License 2.0](./LICENSE)，代码可免费用做商业用途，模型权重和数据只能用于研究目的。请在产品说明中附加Llama3-Chinese的链接和授权协议。
## Citation
如果你在研究中使用了Llama3-Chinese，请按如下格式引用：
```latex
@misc{Llama3-Chinese,
title={Llama3-Chinese},
author={Zhichen Zhang, Xin LU, Long Chen},
year={2024},
howpublished={\url{https://github.com/seanzhang-zhichen/llama3-chinese}},
}
```
## Acknowledgement
[meta-llama/llama3](https://github.com/meta-llama/llama3)
[hiyouga/LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
## Star History
[](https://star-history.com/#seanzhang-zhichen/Llama3-Chinese&Date)
