Instructions to use inferencerlabs/archived-Kimi-K2.6-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use inferencerlabs/archived-Kimi-K2.6-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("inferencerlabs/archived-Kimi-K2.6-MLX") config = load_config("inferencerlabs/archived-Kimi-K2.6-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
Kimi K2.6
See Kimi K2.6 in action: demonstration video
Full-quality MLX conversion of Kimi-K2.6 for use with Inferencer app's distributed compute or model streaming features.
NOTICE - Moved to archive due to storage restrictions: available here
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Model tree for inferencerlabs/archived-Kimi-K2.6-MLX
Base model
moonshotai/Kimi-K2.6