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_hubfrom 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/FastChat.git@v0.1.10
# 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
