Instructions to use starkdmi/SmolVLM2-500M-Video-Instruct-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use starkdmi/SmolVLM2-500M-Video-Instruct-mlx-8bit 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("starkdmi/SmolVLM2-500M-Video-Instruct-mlx-8bit") config = load_config("starkdmi/SmolVLM2-500M-Video-Instruct-mlx-8bit") # 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
8-bit MLX conversion of HuggingFaceTB/SmolVLM2-500M-Video-Instruct, licensed under Apache-2.0 as the original.
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Model size
0.5B params
Tensor type
U32
·
BF16 ·
Hardware compatibility
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8-bit
Model tree for starkdmi/SmolVLM2-500M-Video-Instruct-mlx-8bit
Base model
HuggingFaceTB/SmolLM2-360M Quantized
HuggingFaceTB/SmolLM2-360M-Instruct Quantized
HuggingFaceTB/SmolVLM-500M-Instruct