Instructions to use vinamrag-code/llama-cpp-python-cuda-wheels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use vinamrag-code/llama-cpp-python-cuda-wheels with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="vinamrag-code/llama-cpp-python-cuda-wheels", filename="{{GGUF_FILE}}", )output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
Upload llama_cpp_python-0.3.36+cu130.gemma2-py3-none-linux_x86_64.whl with huggingface_hub
a6acce9 verified Download llama_cpp_python-0.3.36+cu130.gemma2-py3-none-linux_x86_64.whl from vinamrag-code/llama-cpp-python-cuda-wheels: direct link, hf CLI and curl.
- Browser
- Download file 271 MB
-
https://huggingface.co/vinamrag-code/llama-cpp-python-cuda-wheels/resolve/main/llama_cpp_python-0.3.36%2Bcu130.gemma2-py3-none-linux_x86_64.whl
- Command line
-
hf download hf://vinamrag-code/llama-cpp-python-cuda-wheels/llama_cpp_python-0.3.36+cu130.gemma2-py3-none-linux_x86_64.whl
-
curl -L -o llama_cpp_python-0.3.36+cu130.gemma2-py3-none-linux_x86_64.whl https://huggingface.co/vinamrag-code/llama-cpp-python-cuda-wheels/resolve/main/llama_cpp_python-0.3.36%2Bcu130.gemma2-py3-none-linux_x86_64.whl
271 MB
- Xet hash:
- 915026f7c5ef44341c9a8925279071ed10342d01aba75098319d1206a2d1430c
- Size of remote file:
- 271 MB
- SHA256:
- 162d81a2a9458dc3fdd6fbcb0040485f1743336c19720e0f87112e60d1e6105e
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