Text Generation
MLC-LLM
English
webllm
webgpu
browser
recursive-multi-agent
qwen2
latent
conversational
Instructions to use VishalMysore/RecursiveMAS-0.5B-MLC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLC-LLM
How to use VishalMysore/RecursiveMAS-0.5B-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:
- e47815ff1e02fe83f1b4bf49c8cd594ee773d8675dd69e0450b243fe7a4baab3
- Size of remote file:
- 33.2 MB
- SHA256:
- 95878b3c0e86237d7650a4221e8b6717ffadf419fec0a7dbb7143bfc93f7847c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.