💻MAC os Compatible💻
Llama 2 ko 7B - GGUF
- Model creator: Meta
- Original model: Llama 2 7B Chat
- Reference: Llama 2 7B GGUF
Download
pip3 install huggingface-hub>=0.17.1
Then you can download any individual model file to the current directory, at high speed, with a command like this:
huggingface-cli download 24bean/Llama-2-ko-7B-Chat-GGUF llama-2-ko-7b-chat-q8-0.gguf --local-dir . --local-dir-use-symlinks False
Or you can download llama-2-ko-7b.gguf, non-quantized model by
huggingface-cli download 24bean/Llama-2-ko-7B-Chat-GGUF llama-2-ko-7b-chat.gguf --local-dir . --local-dir-use-symlinks False
Example llama.cpp
command
Make sure you are using llama.cpp
from commit d0cee0d36d5be95a0d9088b674dbb27354107221 or later.
./main -ngl 32 -m llama-2-ko-7b-chat-q8-0.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
How to run from Python code
You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.
How to load this model from Python using ctransformers
First install the package
# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
Simple example code to load one of these GGUF models
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("24bean/Llama-2-ko-7B-Chat-GGUF", model_file="llama-2-7b-chat-q8-0.gguf", model_type="llama", gpu_layers=50)
print(llm("인공지능은"))
How to use with LangChain
Here's guides on using llama-cpp-python or ctransformers with LangChain:
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