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
File size: 713 Bytes
f6cd5ac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | {
"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"
}
|