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:
- 213c9f8a5e520aa7a5ab78c1d75f5f65cfe3fb60ff537af7928b46445a8e3a57
- Size of remote file:
- 25.2 MB
- SHA256:
- d326b105d9333ed77bb1218e1f722ee445c508328242bb7647371622853c1b95
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.