Instructions to use larkooo/gemma-e2b-rlcd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use larkooo/gemma-e2b-rlcd with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download larkooo/gemma-e2b-rlcd --local-dir gemma-e2b-rlcd
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download CONTRIBUTING.md from larkooo/gemma-e2b-rlcd: direct link, hf CLI and curl.
- Browser
- Download file 1.12 kB
-
https://huggingface.co/larkooo/gemma-e2b-rlcd/resolve/main/CONTRIBUTING.md
- Command line
-
hf download hf://larkooo/gemma-e2b-rlcd/CONTRIBUTING.md
-
curl -L -o CONTRIBUTING.md https://huggingface.co/larkooo/gemma-e2b-rlcd/resolve/main/CONTRIBUTING.md
Contributing
Follow the README setup, then run:
python -m pytest -q
ruff check gemma_rlcd tests scripts
ruff format --check gemma_rlcd tests scripts
node --check gemma_rlcd/static/app.js
CI runs portable contract and web tests on Linux without model weights. MLX tests and inference require Apple Silicon and run locally.
Keep changes focused. Add tests for changed behavior, preserve each output type's probability semantics, and include answer quality alongside performance measurements. Use the same input, model, and media settings for comparison paths; retain invalid responses in reports.
Training changes should report held-out accuracy, log loss, Brier score, and reliability alongside latency. Fit temperatures on a separate calibration split. See training and calibration for the data format and objectives.
Keep uploads, credentials, model weights, machine paths, and runtime logs out of commits. Use the ignored work/ directory for generated fixtures and new runs. Published reports should identify their data, model revision, settings, and measurement boundaries.