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https://github.com/soulteary/docker-llama2-chat

Play LLaMA2 (official / 中文版 / INT4 / llama2.cpp) Together! ONLY 3 STEPS! ( non GPU / 5GB vRAM / 8~14GB vRAM)
https://github.com/soulteary/docker-llama2-chat

llama llama2 llama2-docker llama2-playground llm

Last synced: 24 days ago
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Play LLaMA2 (official / 中文版 / INT4 / llama2.cpp) Together! ONLY 3 STEPS! ( non GPU / 5GB vRAM / 8~14GB vRAM)

Host: GitHub
URL: https://github.com/soulteary/docker-llama2-chat
Owner: soulteary
License: apache-2.0
Created: 2023-07-19T15:10:44.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2023-08-14T09:21:22.000Z (about 1 year ago)
Last Synced: 2024-10-10T06:33:32.945Z (25 days ago)
Topics: llama, llama2, llama2-docker, llama2-playground, llm
Language: Python
Homepage: https://www.zhihu.com/people/soulteary/posts
Size: 8.55 MB
Stars: 533
Watchers: 6
Forks: 83
Open Issues: 11
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

StarryDivineSky - soulteary/docker-llama2-chat

README

# Docker LLaMA2 Chat / 羊驼二代

中文文档 | ENGLISH

[![](https://img.shields.io/badge/LLaMA2-Official_7B_/_13B-blue)](https://huggingface.co/meta-llama) [![](https://img.shields.io/badge/LLaMA2-Chinese_7B-blue)](https://huggingface.co/soulteary/Chinese-Llama-2-7b-4bit) [![](https://img.shields.io/badge/LLaMA2-Chinese_GGMLQ4-blue)](https://huggingface.co/soulteary/Chinese-Llama-2-7b-ggml-q4) [![](https://img.shields.io/badge/License-Apache_v2-blue)](https://github.com/soulteary/docker-llama2-chat/blob/main/LICENSE)

三步上手 LLaMA2，一起玩！相关博客教程已更新，**同样欢迎“一键三连”** 🌟🌟🌟。

> 使用 Docker 快速上手，本地部署 7B 或 13B 官方模型，或者 7B 中文模型。

### 博客教程

| 类型 | 显存需求 | 特点 | 教程地址 | 教程时间 |
| --- | --- | --- | --- | --- |
| 官方版（英文） | 8~14GB | 原汁原味 | [使用 Docker 快速上手官方版 LLaMA2 开源大模型](https://soulteary.com/2023/07/21/use-docker-to-quickly-get-started-with-the-official-version-of-llama2-open-source-large-model.html) | 2023.07.21 |
| LinkSoul 中文版（双语）| 8~14GB | 支持中文 | [使用 Docker 快速上手中文版 LLaMA2 开源大模型](https://soulteary.com/2023/07/21/use-docker-to-quickly-get-started-with-the-chinese-version-of-llama2-open-source-large-model.html) | 2023.07.21 |
| Transformers 量化（中文/官方） | 5GB | 加速推理、节约显存 | [使用 Transformers 量化 Meta AI LLaMA2 中文版大模型](https://soulteary.com/2023/07/22/quantizing-meta-ai-llama2-chinese-version-large-models-using-transformers.html) | 2023.07.22 |
| GGML (Llama.cpp) 量化（中文/官方）| 可以不需要显存 | CPU 推理 | [构建能够使用 CPU 运行的 MetaAI LLaMA2 中文大模型](https://soulteary.com/2023/07/23/build-llama2-chinese-large-model-that-can-run-on-cpu.html) | 2023.07.23 |

你可以参考项目代码，举一反三，把模型跑起来，接入到你想玩的地方，包括并不局限于支持 LLaMA 1代的各种开源软件中。

## 预览图

![](.github/preview.png)

![](.github/llama2-cn-4bit.jpg)

![](.github/clip.gif)

## 使用方法

1. 一条命令，从项目中构建官方版（7B或13B）模型镜像，或中文版镜像（7B或INT4量化版）：

```bash
# 7B
bash scripts/make-7b.sh

# 或 13B
bash scripts/make-13b.sh

# 或 7B Chinese
bash scripts/make-7b-cn.sh

# 或 7B Chinese 4bit
bash scripts/make-7b-cn-4bit.sh
```

2. 选择适合你的命令，从 HuggingFace 下载 LLaMA2 或中文模型：

```bash
# MetaAI LLaMA2 Models (10~14GB vRAM)
git clone https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
git clone https://huggingface.co/meta-llama/Llama-2-13b-chat-hf

mkdir meta-llama
mv Llama-2-7b-chat-hf meta-llama/
mv Llama-2-13b-chat-hf meta-llama/

# 或 Chinese LLaMA2 (10~14GB vRAM)
git clone https://huggingface.co/LinkSoul/Chinese-Llama-2-7b

mkdir LinkSoul
mv Chinese-Llama-2-7b LinkSoul/

# 或 Chinese LLaMA2 4BIT (5GB vRAM)
git clone https://huggingface.co/soulteary/Chinese-Llama-2-7b-4bit

mkdir soulteary
mv Chinese-Llama-2-7b-4bit soulteary/
```

将下载好的模型，保持在一个正确的目录结构中。

```bash
tree -L 2 meta-llama
soulteary
└── ...
LinkSoul
└── ...
meta-llama
├── Llama-2-13b-chat-hf
│   ├── added_tokens.json
│   ├── config.json
│   ├── generation_config.json
│   ├── LICENSE.txt
│   ├── model-00001-of-00003.safetensors
│   ├── model-00002-of-00003.safetensors
│   ├── model-00003-of-00003.safetensors
│   ├── model.safetensors.index.json
│   ├── pytorch_model-00001-of-00003.bin
│   ├── pytorch_model-00002-of-00003.bin
│   ├── pytorch_model-00003-of-00003.bin
│   ├── pytorch_model.bin.index.json
│   ├── README.md
│   ├── Responsible-Use-Guide.pdf
│   ├── special_tokens_map.json
│   ├── tokenizer_config.json
│   ├── tokenizer.model
│   └── USE_POLICY.md
└── Llama-2-7b-chat-hf
├── added_tokens.json
├── config.json
├── generation_config.json
├── LICENSE.txt
├── model-00001-of-00002.safetensors
├── model-00002-of-00002.safetensors
├── model.safetensors.index.json
├── models--meta-llama--Llama-2-7b-chat-hf
├── pytorch_model-00001-of-00003.bin
├── pytorch_model-00002-of-00003.bin
├── pytorch_model-00003-of-00003.bin
├── pytorch_model.bin.index.json
├── README.md
├── special_tokens_map.json
├── tokenizer_config.json
├── tokenizer.json
├── tokenizer.model
└── USE_POLICY.md
```

3. 选择使用下面的适合你的命令，一键运行 LLaMA2 模型应用：

```bash
# 7B
bash scripts/run-7b.sh
# 或 13B
bash scripts/run-13b.sh
# 或 Chinese 7B
bash scripts/run-7b-cn.sh
# 或 Chinese 7B 4BIT
bash scripts/run-7b-cn-4bit.sh
```

模型运行之后，在浏览器中访问 `http://localhost7860` 或者 `http://你的IP地址:7860` 就可以开始玩了。

## 相关项目

- MetaAI LLaMA2: https://ai.meta.com/llama/ ❤️
- Meta LLaMA2 7B Chat: https://huggingface.co/meta-llama/Llama-2-7b-chat
- Meta LLaMA2 13B Chat: https://huggingface.co/meta-llama/Llama-2-13b-chat
- Chinese LLaMA2 7B: https://huggingface.co/LinkSoul/Chinese-Llama-2-7b ❤️
- Chinese LLaMA2 7B GGML q4: https://huggingface.co/soulteary/Chinese-Llama-2-7b-ggml-q4
- LLaMA2 GGML Converter: https://hub.docker.com/r/soulteary/llama2