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 docs/assets/README.md from larkooo/gemma-e2b-rlcd: direct link, hf CLI and curl.
- Browser
- Download file 845 Bytes
-
https://huggingface.co/larkooo/gemma-e2b-rlcd/resolve/main/docs/assets/README.md
- Command line
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hf download hf://larkooo/gemma-e2b-rlcd/docs/assets/README.md
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curl -L -o README.md https://huggingface.co/larkooo/gemma-e2b-rlcd/resolve/main/docs/assets/README.md
Demo assets
demo.mp4 is a 24-second, 1080p, silent replay of the measured support-triage comparison. Both clocks use real elapsed-time scale; each answer appears when its measured median completion time elapses. It is a designed replay rather than a screen recording. Eight representative fields are shown; all 28 outputs are in demo-metrics.json.
The source is support_28 in the workload report: four measured repetitions per path after warmup, 4.4145 seconds parallel versus 15.6597 seconds normal generation. All 28 outputs match; both paths pass 15/17 annotated checks. The metrics file retains the report hash and complete methodology.
The video uses IBM Plex Sans under the SIL Open Font License. The repository URL and measured results appear in the closing frame.