Instructions to use EZCon/SmolVLM2-2.2B-Instruct-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use EZCon/SmolVLM2-2.2B-Instruct-mlx 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("EZCon/SmolVLM2-2.2B-Instruct-mlx") config = load_config("EZCon/SmolVLM2-2.2B-Instruct-mlx") # 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 EZCon/SmolVLM2-2.2B-Instruct-mlx: direct link, hf CLI and curl.
- Browser
- Download file 713 Bytes
-
https://huggingface.co/EZCon/SmolVLM2-2.2B-Instruct-mlx/resolve/main/processor_config.json
- Command line
-
hf download hf://EZCon/SmolVLM2-2.2B-Instruct-mlx/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/EZCon/SmolVLM2-2.2B-Instruct-mlx/resolve/main/processor_config.json
713 Bytes
| { | |
| "image_processor": { | |
| "do_convert_rgb": true, | |
| "do_image_splitting": true, | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "SmolVLMImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "max_image_size": { | |
| "longest_edge": 384 | |
| }, | |
| "resample": 1, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "longest_edge": 1536 | |
| }, | |
| "video_sampling": { | |
| "fps": 1, | |
| "max_frames": 64, | |
| "video_size": { | |
| "longest_edge": 384 | |
| } | |
| } | |
| }, | |
| "image_seq_len": 81, | |
| "processor_class": "SmolVLMProcessor" | |
| } | |