深度学习笔记--本地部署Mini-GPT4

目录

1--前言

2--配置环境依赖

3--下载权重

4--生成 Vicuna 权重

5--测试

6--可能出现的问题

---

1--前言

本机环境：

        System: Ubuntu 18.04

        GPU:  Tesla V100 (32G)

        CUDA: 10.0

 项目地址：https://github.com/Vision-CAIR/MiniGPT-4

2--配置环境依赖

git clone https://github.com/Vision-CAIR/MiniGPT-4.git
cd MiniGPT-4
conda env create -f environment.yml
conda activate minigpt4

        默认配置的环境名为 minigpt4，也可以通过 environment.yml 来修改环境名，这里博主设置的 python 环境为 ljf_minigpt4：

3--下载权重

        这里博主选用的 LLaMA 权重为 llama-7b-hf，Vicuna 增量文件为 vicuna-7b-delta-v1.1，对应的下载地址如下：

LLaMA 权重下载地址

Vicuna 增量文件下载地址

        这里提供两种下载方式，第一种是基于 huggingface_hub 第三方库进行下载：

pip install huggingface_hub

from huggingface_hub import snapshot_download
snapshot_download(repo_id='decapoda-research/llama-7b-hf')
# 对应的存储地址为：~/.cache/huggingface/hub/models--decapoda-research--llama-7b-hf/snapshots/(一串数字)/

from huggingface_hub import snapshot_download
snapshot_download(repo_id='lmsys/vicuna-7b-delta-v1.1')
# 对应的存储地址为:~.cache/huggingface/hub/models--lmsys--vicuna-7b-delta-v1.1/snapshots/(一串数字)/

        第一种下载方式容易出现连接超时的错误，这里提供第二种基于 wget 的下载方式：

# 记录每一个文件的下载url，使用 wget 来下载，download.txt 的内容如下：
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/.gitattributes
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/LICENSE
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/README.md
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/config.json
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/generation_config.json
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00001-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00002-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00003-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00004-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00005-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00006-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00007-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00008-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00009-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00010-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00011-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00012-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00013-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00014-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00015-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00016-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00017-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00018-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00019-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00020-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00021-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00022-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00023-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00024-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00025-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00026-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00027-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00028-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00029-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00030-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00031-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00032-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model-00033-of-00033.bin
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/pytorch_model.bin.index.json
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/special_tokens_map.json
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/tokenizer.model
https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/tokenizer_config.json

        编写 download.sh，下载 download.txt 中所有的文件：

#! /bin/bash
while read file; do
	wget ${file}
done < download.txt

        同理对于增量文件，记录所有文件的下载 url，通过 wget 下载：

https://huggingface.co/lmsys/vicuna-7b-delta-v1.1/resolve/main/.gitattributes
https://huggingface.co/lmsys/vicuna-7b-delta-v1.1/resolve/main/README.md
https://huggingface.co/lmsys/vicuna-7b-delta-v1.1/resolve/main/config.json
https://huggingface.co/lmsys/vicuna-7b-delta-v1.1/resolve/main/generation_config.json
https://huggingface.co/lmsys/vicuna-7b-delta-v1.1/resolve/main/pytorch_model-00001-of-00002.bin
https://huggingface.co/lmsys/vicuna-7b-delta-v1.1/resolve/main/pytorch_model-00002-of-00002.bin
https://huggingface.co/lmsys/vicuna-7b-delta-v1.1/resolve/main/pytorch_model.bin.index.json
https://huggingface.co/lmsys/vicuna-7b-delta-v1.1/resolve/main/special_tokens_map.json
https://huggingface.co/lmsys/vicuna-7b-delta-v1.1/resolve/main/tokenizer.model
https://huggingface.co/lmsys/vicuna-7b-delta-v1.1/resolve/main/tokenizer_config.json

#! /bin/bash
while read file; do
	wget ${file}
done < download.txt

4--生成 Vicuna 权重

        安装 FastChat 的 python 库：

pip install git+https://github.com/lm-sys/[email protected]
# or
pip install fschat

        终端执行以下命令，生成最终的权重文件：

python3 -m fastchat.model.apply_delta \
    --base-model-path llama-7b-hf_path \
    --target-model-path vicuna-7b_path \
    --delta-path vicuna-7b-delta-v1.1_path

--bash-model-path 表示第 3 步中下载的 llama-7b-hf 权重的存放地址；

--target-model-path 表示生成 Vicuna 权重的存放地址；

--delta-path 表示第 3 步中下载的 vicuna-7b-delta-v1.1 权重的存放地址；

5--测试

        首先下载测试权重，这里博主选用的是 Checkpoint Aligned with Vicuna 7B：

Checkpoint Aligned with Vicuna 13B 下载地址

Checkpoint Aligned with Vicuna 7B 下载地址

        接着配置测试文件：

        修改 MiniGPT-4/eval_configs/minigpt4_eval.yaml 配置文件中的 ckpt 路径为 Checkpoint Aligned with Vicuna 7B 的地址：

        修改 MiniGPT-4/minigpt4/configs/models/minigpt4.yaml 配置文件中的 llama_model 为第4步生成 Vicuna 权重的存放地址：

        执行以下命令启动网页端的 Mini-GPT4：

python demo.py --cfg-path eval_configs/minigpt4_eval.yaml  --gpu-id 0

        在本机打开 local url 即可，也可以使用其它电脑打开 public url：当然也可以修改提供的demo，无需使用网页端直接在终端测试推理结果：

6--可能出现的问题

① name ‘cuda_setup’ is not defined： 

解决方法：

        升级 bitsandbytes 库，这里博主选用 0.38.1 版本的 bitsandbytes 解决了问题；

pip install bitsandbytes==0.38.1