Instructions to use mlc-ai/gemma-3-1b-it-q0f32-MLC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLC-LLM
How to use mlc-ai/gemma-3-1b-it-q0f32-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
Download params_shard_17.bin from mlc-ai/gemma-3-1b-it-q0f32-MLC: direct link, hf CLI and curl.
- Browser
- Download file 31.9 MB
-
https://huggingface.co/mlc-ai/gemma-3-1b-it-q0f32-MLC/resolve/2b50200b152e363d0f80e397629e5794d2c5e85c/params_shard_17.bin
- Command line
-
hf download hf://mlc-ai/gemma-3-1b-it-q0f32-MLC@2b50200b152e363d0f80e397629e5794d2c5e85c/params_shard_17.bin
-
curl -L -o params_shard_17.bin https://huggingface.co/mlc-ai/gemma-3-1b-it-q0f32-MLC/resolve/2b50200b152e363d0f80e397629e5794d2c5e85c/params_shard_17.bin
31.9 MB
- Xet hash:
- 13a5a79fd342084dc56887b3e2a6926ded4a6ba383d25096e3b54df6b2329634
- Size of remote file:
- 31.9 MB
- SHA256:
- b1253e760de9d9ba6f50dc1b127641f7089e6cb0653dd7fafc577a47df74c6b9
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