Instructions to use Dadm-n/InternVL3_5-2B-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Dadm-n/InternVL3_5-2B-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("Dadm-n/InternVL3_5-2B-mlx") config = load_config("Dadm-n/InternVL3_5-2B-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 preprocessor_config.json from Dadm-n/InternVL3_5-2B-mlx: direct link, hf CLI and curl.
- Browser
- Download file 666 Bytes
-
https://huggingface.co/Dadm-n/InternVL3_5-2B-mlx/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Dadm-n/InternVL3_5-2B-mlx/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Dadm-n/InternVL3_5-2B-mlx/resolve/main/preprocessor_config.json
666 Bytes
| { | |
| "crop_size": null, | |
| "crop_to_patches": false, | |
| "data_format": "channels_first", | |
| "default_to_square": true, | |
| "device": null, | |
| "do_center_crop": null, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_processor_type": "GotOcr2ImageProcessorFast", | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "input_data_format": null, | |
| "max_patches": 12, | |
| "min_patches": 1, | |
| "processor_class": "InternVLProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_tensors": null, | |
| "size": { | |
| "height": 448, | |
| "width": 448 | |
| } | |
| } | |