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_2.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_2.bin
- Command line
-
hf download hf://mlc-ai/gemma-3-1b-it-q0bf16-MLC@25dbd247ee7b256ce2a817bf93e19c92387f1526/params_shard_2.bin
-
curl -L -o params_shard_2.bin https://huggingface.co/mlc-ai/gemma-3-1b-it-q0bf16-MLC/resolve/25dbd247ee7b256ce2a817bf93e19c92387f1526/params_shard_2.bin
21.8 MB
- Xet hash:
- 672240796851a9e797d3fd4787577797fa3433af4aac12de61b34be9e807e74f
- Size of remote file:
- 21.8 MB
- SHA256:
- 29b02851cf25fde0e0a4d6316740f8bc14da50d45a776a7ce0ff57eb975f3a1f
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