初始化项目，由ModelHub XC社区提供模型
Model: seanzhang/Llama3-Chinese Source: Original Platform
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ModelHub XC
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README_CN.md
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README_CN.md
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<p align="left">
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中文</a>  ｜  <a href="README.md">English</a>
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</p>
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<br><br>
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<p align="center">
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<a href='https://huggingface.co/spaces/zhichen'>
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<img src='./images/logo.png'>
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</a>
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</p>
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<div align="center">
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<p align="center">
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<h3> Llama3-Chinese </h3>
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<p align="center">
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<a href='https://huggingface.co/zhichen'>
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<img src='https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Llama3%20Chinese-yellow'>
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</a>
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<a href='https://modelscope.cn/profile/seanzhang'>
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<img src='https://img.shields.io/badge/🤖 ModelScope-Llama3%20Chinese-blue'>
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</a>
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<br>
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<a href=href="https://github.com/seanzhang-zhichen/llama3-chinese/stargazers">
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<img src="https://img.shields.io/github/stars/seanzhang-zhichen/llama3-chinese?color=ccf">
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</a>
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<a href="https://github.com/seanzhang-zhichen/llama3-chinese/blob/main/LICENSE">
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<img alt="GitHub Contributors" src="https://img.shields.io/badge/license-Apache%202.0-blue.svg" />
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</a>
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</p>
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</div>
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## 介绍
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**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。
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**Github:** [https://github.com/seanzhang-zhichen/llama3-chinese.git](https://github.com/seanzhang-zhichen/llama3-chinese.git)
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## 模型下载
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| Model | Download |
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|:-------------------:|:-----------:|
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| 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)|
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| Llama3-Chinese-Lora |[ 🤗 HuggingFace](https://huggingface.co/zhichen/Llama3-Chinese-Lora) [ 🤖 ModelScope](https://modelscope.cn/models/seanzhang/Llama3-Chinese-Lora)|
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| Llama3-Chinese (合并好的模型) |[ 🤗 HuggingFace](https://huggingface.co/zhichen/Llama3-Chinese) [ 🤖 ModelScope](https://modelscope.cn/models/seanzhang/Llama3-Chinese)|
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## 合并LORA模型（可跳过）
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1、下载 [Meta-Llama-3-8B](https://modelscope.cn/models/LLM-Research/Meta-Llama-3-8B)
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```bash
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git clone https://www.modelscope.cn/LLM-Research/Meta-Llama-3-8B.git
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```
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2、下载[Llama3-Chinese-Lora](https://www.modelscope.cn/models/seanzhang/Llama3-Chinese-Lora)
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**From ModelScope**
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```bash
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git lfs install
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git clone https://www.modelscope.cn/seanzhang/Llama3-Chinese-Lora.git
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```
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**From HuggingFace**
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```bash
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git lfs install
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git clone https://huggingface.co/zhichen/Llama3-Chinese-Lora
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```
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3、合并模型
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```bash
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python merge_lora.py \
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--base_model path/to/Meta-Llama-3-8B \
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--lora_model path/to/lora/Llama3-Chinese-Lora \
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--output_dir ./Llama3-Chinese
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```
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## 下载 Llama3-Chinese（合并好的模型）
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**From ModelScope**
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```bash
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git lfs install
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git clone https://www.modelscope.cn/seanzhang/Llama3-Chinese.git
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```
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**From HuggingFace**
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```bash
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git lfs install
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git clone https://huggingface.co/zhichen/Llama3-Chinese
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```
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## 推理
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "zhichen/Llama3-Chinese"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "你好"},
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]
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input_ids = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True, return_tensors="pt"
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).to(model.device)
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outputs = model.generate(
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input_ids,
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max_new_tokens=2048,
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do_sample=True,
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temperature=0.7,
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top_p=0.95,
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)
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response = outputs[0][input_ids.shape[-1]:]
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print(tokenizer.decode(response, skip_special_tokens=True))
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```
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## 命令行推理
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```bash
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python cli_demo.py --model_path zhichen/Llama3-Chinese
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```
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## web推理
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```bash
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python web_demo.py --model_path zhichen/Llama3-Chinese
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```
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## vllm web 推理
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1、使用[vllm](https://github.com/vllm-project/vllm)部署模型
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```bash
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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)
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```
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2、在命令行执行
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```bash
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python vllm_web_demo.py --model Llama3-Chinese
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```
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## 训练数据集
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[匠数科技大模型sft数据集](https://modelscope.cn/datasets/deepctrl/deepctrl-sft-data)
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## LICENSE
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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)。
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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。
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## Citation
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如果你在研究中使用了Llama3-Chinese，请按如下格式引用：
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```latex
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@misc{Llama3-Chinese,
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title={Llama3-Chinese},
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author={Zhichen Zhang, Xin LU, Long Chen},
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year={2024},
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howpublished={\url{https://github.com/seanzhang-zhichen/llama3-chinese}},
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}
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```
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## Acknowledgement
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[meta-llama/llama3](https://github.com/meta-llama/llama3)
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<br>
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[hiyouga/LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
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## Star History
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[](https://star-history.com/#seanzhang-zhichen/Llama3-Chinese&Date)
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