Instructions to use mlc-ai/gemma-3-1b-it-q0bf16-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-q0bf16-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_32.bin from mlc-ai/gemma-3-1b-it-q0bf16-MLC: direct link, hf CLI and curl.
- Browser
- Download file 21.8 MB
-
https://huggingface.co/mlc-ai/gemma-3-1b-it-q0bf16-MLC/resolve/25dbd247ee7b256ce2a817bf93e19c92387f1526/params_shard_32.bin
- Command line
-
hf download hf://mlc-ai/gemma-3-1b-it-q0bf16-MLC@25dbd247ee7b256ce2a817bf93e19c92387f1526/params_shard_32.bin
-
curl -L -o params_shard_32.bin https://huggingface.co/mlc-ai/gemma-3-1b-it-q0bf16-MLC/resolve/25dbd247ee7b256ce2a817bf93e19c92387f1526/params_shard_32.bin
21.8 MB
- Xet hash:
- 1f5c1be744f814db65adbe8385597bcf1e1cd091c1bc8ba674c1444d548de25c
- Size of remote file:
- 21.8 MB
- SHA256:
- ad0445e679c798e517666a2f2de7491793268f538df8f4cd4e77d233d10cf0ea
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