Instructions to use raining-codes/Gemma3-1B-LOMO-q4f16_1-MLC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLC-LLM
How to use raining-codes/Gemma3-1B-LOMO-q4f16_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
Gemma3-1B-LOMO-q4f16_1-MLC
MLC-LLM formatted weights for on-device inference.
- Conv template:
gemma3_instruction(runtime prompt formatting should match training) - Files
mlc-chat-config.jsonparams_shard_*.bintensor-cache.json- tokenizer files (
tokenizer.json+tokenizer.modelorvocab.json+merges.txt)
Quick test (CLI)
mlc_llm chat HF://raining-codes/Gemma3-1B-LOMO-q4f16_1-MLC --temperature 0.7 --top-p 0.9 --repeat-penalty 1.08 --max-gen-len 512
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