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 reports/encoder-check.json from larkooo/gemma-e2b-rlcd: direct link, hf CLI and curl.
- Browser
- Download file 608 Bytes
-
https://huggingface.co/larkooo/gemma-e2b-rlcd/resolve/main/reports/encoder-check.json
- Command line
-
hf download hf://larkooo/gemma-e2b-rlcd/reports/encoder-check.json
-
curl -L -o encoder-check.json https://huggingface.co/larkooo/gemma-e2b-rlcd/resolve/main/reports/encoder-check.json
608 Bytes
| { | |
| "compute_dtype": "float16", | |
| "state_layers": 4, | |
| "results": [ | |
| { | |
| "state": "text", | |
| "native_layer_max_abs_delta": 0.0, | |
| "dense_vs_indexed_embedding_max_abs_delta": 0.0 | |
| }, | |
| { | |
| "state": "image", | |
| "native_layer_max_abs_delta": 0.0, | |
| "dense_vs_indexed_embedding_max_abs_delta": 0.0 | |
| }, | |
| { | |
| "state": "speech", | |
| "native_layer_max_abs_delta": 0.0, | |
| "dense_vs_indexed_embedding_max_abs_delta": 0.0 | |
| }, | |
| { | |
| "state": "video_speech", | |
| "native_layer_max_abs_delta": 0.0, | |
| "dense_vs_indexed_embedding_max_abs_delta": 0.0 | |
| } | |
| ] | |
| } | |