Instructions to use welcoma/Ternary-Bonsai-1.7B-bonsai_tq_f32-MLC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLC-LLM
How to use welcoma/Ternary-Bonsai-1.7B-bonsai_tq_f32-MLC with MLC-LLM:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4bbbca834ab3aa88aa8c7764e6e16d13f19ec05d585bc46aba198261e9cfb5ae
- Size of remote file:
- 33.4 MB
- SHA256:
- 4c24c285fa91f256e3266744548d28439e3e087b6d605cd9368f3e0f0bb9db47
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.