Instructions to use inferencerlabs/Intern-S2-Preview-397B-MLX-Q9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use inferencerlabs/Intern-S2-Preview-397B-MLX-Q9 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("inferencerlabs/Intern-S2-Preview-397B-MLX-Q9") config = load_config("inferencerlabs/Intern-S2-Preview-397B-MLX-Q9") # 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
Intern-S2-Preview-397B
No longer available on HF due to storage restrictions - archived here
See Intern-S2-Preview-397B in action: demonstration videos
Tested with an M3 Ultra 512 GiB using Inferencer app v2.2.2
- Text Inference: ~25.3 tokens/s @ 1000 tokens ~415.4 GiB
- Vision Inference: ~23.4 tokens/s ~416.2 GiB
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internlm/Intern-S2-Preview-397B