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
- Xet hash:
- 9a632633b0d91f15eb5a649e3484c9d60e77eea5380273468f0b310a9947965f
- Size of remote file:
- 11.4 MB
- SHA256:
- 6581c44164d273d4222df982905a7e0450dcf3a4a7ebe98f9ec53e4de05beffe
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.