网站的设计理念怎么做网站排名

作者：张华 发表于：2023-09-06

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( http://blog.csdn.net/quqi99 )

据说现在在PC机上可以运行llama2大模型了，

Way 1

于是照着这个文档[1] 试了一下：

#https://medium.com/@karankakwani/build-and-run-llama2-llm-locally-a3b393c1570e
git clone https://github.com/facebookresearch/llama.git
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make
#request access - https://ai.meta.com/resources/models-and-libraries/llama-downloads/
#then run 'cd llama && ./download.sh', and select 7B-chat to download
du -h ./llama-2-7b-chat/consolidated.00.pth
cd ../llama.cpp/
python3 -m venv llama2
source llama2/bin/activate
python3 -m pip install -r requirements.txt
mkdir -p models/7B
python3 convert.py --outfile models/7B/ggml-model-f16.bin --outtype f16 ../../llama2/llama/llama-2-7b-chat --vocab-dir ../../llama2/llama

但是在上面转换成f16模式的时候报了一个python的错误，像是这个bug (https://github.com/huggingface/transformers/issues/20011 ), 但解决不了。

Way 2

于是，接着转向使用xinference框架在本地试，成功了，就是在nuc上(i7-13700H)速度挺慢的.

python3 -m venv xinference
source xinference/bin/activate
pip install "xinference[all]"
xinference -H 192.168.99.179

Way 3

#https://github.com/PromtEngineer/localGPT
wget -c https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
chmod +x Miniconda3-latest-Linux-x86_64.sh 
./Miniconda3-latest-Linux-x86_64.sh -b
export PATH="/home/hua/miniconda3/bin:"$PATH
conda
conda create -n localGPT
conda env list
conda init bash
conda activate localGPT
cd /bak/work/ai
git clone https://github.com/PromtEngineer/localGPT.git
cd localGPT
pip install -r requirements.txt
cp /nas/book/计算机网络.pdf /bak/work/ai/localGPT/SOURCE_DOCUMENTS/
#need to use proxychains to access huggingface.co:443 and cdn-lfs.huggingface.co:443 to download pytorch_model.bin etc
#proxychains python ingest.py
proxychains python ingest.py --device_type cpu
proxychains python run_localGPT.py

[1] https://medium.com/@karankakwani/build-and-run-llama2-llm-locally-a3b393c1570e