Instructions to use welcoma/Ternary-Bonsai-8B-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-8B-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:
- abf75ef209210f087b9e11beb2fb9d57335067a5b5b328bc1a5563a40db0a16c
- Size of remote file:
- 33.4 MB
- SHA256:
- 3cda9167eef79fa84a50b35f2c63a957a79f697a20a50b7747ef723bc06f3e19
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.