Any-to-Any
MLX
Safetensors
gemma4
mlx-vlm
rlcd
multimodal
classification
parallel-inference
image-text-to-text
audio
video
4-bit precision
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 gemma_rlcd/__init__.py from larkooo/gemma-e2b-rlcd: direct link, hf CLI and curl.
- Browser
- Download file 226 Bytes
-
https://huggingface.co/larkooo/gemma-e2b-rlcd/resolve/main/gemma_rlcd/__init__.py
- Command line
-
hf download hf://larkooo/gemma-e2b-rlcd/gemma_rlcd/__init__.py
-
curl -L -o __init__.py https://huggingface.co/larkooo/gemma-e2b-rlcd/resolve/main/gemma_rlcd/__init__.py
226 Bytes
| """Gemma E2B RLCD: parallel multimodal classification and grading.""" | |
| from .core import Choice, DecisionEngine, Independent, Noul, Score, State | |
| __all__ = ["Choice", "DecisionEngine", "Independent", "Noul", "Score", "State"] | |