Image-Text-to-Text
MLX
Safetensors
gemma3
multimodal
vision
quantized
pruned
on-device
apple-silicon
conversational
Instructions to use AtomGradient/gemma-3-4b-it-qat-4bit-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AtomGradient/gemma-3-4b-it-qat-4bit-lite with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("AtomGradient/gemma-3-4b-it-qat-4bit-lite") config = load_config("AtomGradient/gemma-3-4b-it-qat-4bit-lite") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download processor_config.json from AtomGradient/gemma-3-4b-it-qat-4bit-lite: direct link, hf CLI and curl.
- Browser
- Download file 70 Bytes
-
https://huggingface.co/AtomGradient/gemma-3-4b-it-qat-4bit-lite/resolve/main/processor_config.json
- Command line
-
hf download hf://AtomGradient/gemma-3-4b-it-qat-4bit-lite/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/AtomGradient/gemma-3-4b-it-qat-4bit-lite/resolve/main/processor_config.json
70 Bytes
| { | |
| "image_seq_length": 256, | |
| "processor_class": "Gemma3Processor" | |
| } | |