Feature Extraction
MLX
Safetensors
Transformers
kimi_k25
quantization
dq3
custom_code
4-bit precision
Instructions to use cs2764/Kimi-K2.6_dq3-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use cs2764/Kimi-K2.6_dq3-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download cs2764/Kimi-K2.6_dq3-mlx --local-dir Kimi-K2.6_dq3-mlx
- Transformers
How to use cs2764/Kimi-K2.6_dq3-mlx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cs2764/Kimi-K2.6_dq3-mlx", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("cs2764/Kimi-K2.6_dq3-mlx", trust_remote_code=True) model = AutoModel.from_pretrained("cs2764/Kimi-K2.6_dq3-mlx", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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Download README.md from cs2764/Kimi-K2.6_dq3-mlx: direct link, hf CLI and curl.
- Browser
- Download file 847 Bytes
-
https://huggingface.co/cs2764/Kimi-K2.6_dq3-mlx/resolve/main/README.md
- Command line
-
hf download hf://cs2764/Kimi-K2.6_dq3-mlx/README.md
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curl -L -o README.md https://huggingface.co/cs2764/Kimi-K2.6_dq3-mlx/resolve/main/README.md
847 Bytes
metadata
tags:
- mlx
- transformers
- quantization
- dq3
Kimi-K2.6_dq3
This model is a DQ3 quantized version of the original model moonshotai/Kimi-K2.6.
It was quantized locally using the mlx_lm library.
Quantization Methodology (DQ3)
This model was quantized using the dynamic DQ3 (3-bit / 4-bit / 8-bit mixed) approach, inspired by the methodology described in the mlx-community/Kimi-K2.5-mlx-DQ3_K_M-q8 repository.
The weights are mixed based on MLX layers:
- Expert layers (switch_mlp / mlp) are quantized to 3-bit.
- The first 5 layers are kept at higher quality (5-bit).
- Every 5th layer is medium quality (4-bit).
- All other layers (e.g. attention, normalization) remain at 8-bit to serve as the "8-bit brain".