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47 lines
1.4 KiB
Markdown
47 lines
1.4 KiB
Markdown
# Using llama.cpp in the web UI
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## Setting up the models
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#### Pre-converted
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Place the model in the `models` folder, making sure that its name contains `ggml` somewhere and ends in `.bin`.
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#### Convert LLaMA yourself
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Follow the instructions in the llama.cpp README to generate the `ggml-model.bin` file: https://github.com/ggerganov/llama.cpp#usage
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## GPU acceleration
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Enabled with the `--n-gpu-layers` parameter.
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* If you have enough VRAM, use a high number like `--n-gpu-layers 200000` to offload all layers to the GPU.
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* Otherwise, start with a low number like `--n-gpu-layers 10` and then gradually increase it until you run out of memory.
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To use this feature, you need to manually compile and install `llama-cpp-python` with GPU support.
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#### Linux
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```
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pip uninstall -y llama-cpp-python
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CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python --no-cache-dir
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```
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#### Windows
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```
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pip uninstall -y llama-cpp-python
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set CMAKE_ARGS="-DLLAMA_CUBLAS=on"
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set FORCE_CMAKE=1
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pip install llama-cpp-python --no-cache-dir
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```
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Here you can find the different compilation options for OpenBLAS / cuBLAS / CLBlast: https://pypi.org/project/llama-cpp-python/
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## Performance
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This was the performance of llama-7b int4 on my i5-12400F (cpu only):
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> Output generated in 33.07 seconds (6.05 tokens/s, 200 tokens, context 17)
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You can change the number of threads with `--threads N`.
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