Instructions to use aidiffuser/Kimi-K2.7-Code-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use aidiffuser/Kimi-K2.7-Code-vision with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Kimi-K2.7-Code-vision aidiffuser/Kimi-K2.7-Code-vision
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Kimi-K2.7-Code-vision
Vision-only weights (MoonViT tower + multimodal projector) extracted from moonshotai/Kimi-K2.7-Code for use with MLX-based inference stacks such as exo, in the same format as exolabs/Kimi-K2.6-vision.
Contents
kimi_k27_vision.safetensorsโ all 335vision_tower.*andmm_projector.*tensors from the official repo (shards 63โ64), original bfloat16, unmodified.config.jsonโ vision config copied from the officialconfig.json(verified byte-identical to Kimi-K2.6's vision config: 27-layer MoonViT, hidden 1152, patch 14,sd2_tpoolmerger, projector to 7168).extract_vision_weights.pyโ the script used to produce this repo, for reproducibility.
Usage with exo
Add a model card for moonshotai/Kimi-K2.7-Code with:
capabilities = ["text", "thinking", "thinking_toggle", "vision"]
[vision]
image_token_id = 163605
model_type = "kimi_vl"
weights_repo = "aidiffuser/Kimi-K2.7-Code-vision"
processor_repo = "moonshotai/Kimi-K2.7-Code"
Tested working: distributed (2ร Mac Studio M3 Ultra, tensor parallelism) with the official INT4 text weights, image understanding confirmed.
License
Same Modified MIT license as the source model; these are a subset of the original weights, unmodified. All credit to Moonshot AI.
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moonshotai/Kimi-K2.7-Code