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 model.safetensors from larkooo/gemma-e2b-rlcd: direct link, hf CLI and curl.
- Browser
- Download file 3.55 GB
-
https://huggingface.co/larkooo/gemma-e2b-rlcd/resolve/main/model.safetensors
- Command line
-
hf download hf://larkooo/gemma-e2b-rlcd/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/larkooo/gemma-e2b-rlcd/resolve/main/model.safetensors
3.55 GB
- Xet hash:
- 2fc6da28e02057781742ad1e173a497fad1394a733c45adea55a5662cb1863c6
- Size of remote file:
- 3.55 GB
- SHA256:
- 038e39a37a7667373d2c3991375446b10c96ae1d717a68674870343db376b76e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.