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:
- cc4c2fb40714d14ee10c47ecfaa3a58d24bee457b9d79f54e044d2bfe01ea006
- Size of remote file:
- 33.1 MB
- SHA256:
- 6faeabc479ded2e93ccb401f20ef36683132cdce08da9418bb32e0e129ee634a
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