在MBP上运行推理LLaMA-7B模型

# build this repo
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp

# obtain the original LLaMA model weights and place them in ./models
ls ./models
65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model

# install Python dependencies
python3 -m pip install torch numpy sentencepiece

# convert the 7B model to ggml FP16 format
python3 convert-pth-to-ggml.py models/7B/ 1

# quantize the model to 4-bits
./quantize.sh 7B

# run the inference
./main -m ./models/7B/ggml-model-q4_0.bin -t 8 -n 128

互动模式

如果您想要更像ChatGPT的体验，您可以通过传递-i作为参数在交互模式下运行。在此模式下，您始终可以通过按Ctrl+C来中断生成，并输入一行或多行文本，这些文本将被转换为令牌并附加到当前上下文中。您还可以使用参数-r "reverse prompt string"指定反向提示。每当在生成中遇到反向提示字符串的确切令牌时，这将导致用户输入被提示。一个典型的用途是使用提示，使LLaMa模拟多个用户之间的聊天，比如Alice和Bob，并通过-r "Alice:"

这是一个用命令调用的交互示例：

./main -m ./models/7B/ggml-model-q4_0.bin -t 8 -n 256 --repeat_penalty 1.0 --color -i -r "User:" \
                                           -p \
"Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.

User: Hello, Bob.
Bob: Hello. How may I help you today?
User: Please tell me the largest city in Europe.
Bob: Sure. The largest city in Europe is Moscow, the capital of Russia.
User:"