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 processor_config.json from Dadm-n/InternVL3_5-2B-mlx: direct link, hf CLI and curl.
- Browser
- Download file 72 Bytes
-
https://huggingface.co/Dadm-n/InternVL3_5-2B-mlx/resolve/main/processor_config.json
- Command line
-
hf download hf://Dadm-n/InternVL3_5-2B-mlx/processor_config.json
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curl -L -o processor_config.json https://huggingface.co/Dadm-n/InternVL3_5-2B-mlx/resolve/main/processor_config.json
72 Bytes
| { | |
| "image_seq_length": 256, | |
| "processor_class": "InternVLProcessor" | |
| } | |