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:
- 40f02536af42ce63b47f7ad2dbf94ba543037914652997bef7aed939acec3b1a
- Size of remote file:
- 20.5 MB
- SHA256:
- 7861247d94e5b0dbf4d241a0a65e23dafb0a4ed57de723e8935eb385d0f85718
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