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] huggingface-cli download --local-dir gemma-e2b-rlcd larkooo/gemma-e2b-rlcd
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download tokenizer.json from larkooo/gemma-e2b-rlcd: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/larkooo/gemma-e2b-rlcd/resolve/main/tokenizer.json
- Command line
-
hf download hf://larkooo/gemma-e2b-rlcd/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/larkooo/gemma-e2b-rlcd/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- c62336ad134cad6f154d84eb0e5a5fa9ca17cd665ef3ba5ac4fd02b1486760b4
- Size of remote file:
- 32.2 MB
- SHA256:
- cc8d3a0ce36466ccc1278bf987df5f71db1719b9ca6b4118264f45cb627bfe0f
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