Instructions to use mlc-ai/gemma-2b-it-q4f32_1-MLC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLC-LLM
How to use mlc-ai/gemma-2b-it-q4f32_1-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_35.bin from mlc-ai/gemma-2b-it-q4f32_1-MLC: direct link, hf CLI and curl.
- Browser
- Download file 33.6 MB
-
https://huggingface.co/mlc-ai/gemma-2b-it-q4f32_1-MLC/resolve/dd69350cb54735c1d6080def9dda777ebbb6ad10/params_shard_35.bin
- Command line
-
hf download hf://mlc-ai/gemma-2b-it-q4f32_1-MLC@dd69350cb54735c1d6080def9dda777ebbb6ad10/params_shard_35.bin
-
curl -L -o params_shard_35.bin https://huggingface.co/mlc-ai/gemma-2b-it-q4f32_1-MLC/resolve/dd69350cb54735c1d6080def9dda777ebbb6ad10/params_shard_35.bin
33.6 MB
- Xet hash:
- 7904d5dfdb1a908ceb46b4098a234c38608432094b884fd922abd8f722a57322
- Size of remote file:
- 33.6 MB
- SHA256:
- babba77a6a349e4b2bf792d59d6f3f661ba9f6b8576d0661d867caa9ddd1ee4e
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