README.md

---
tags:
- mlx
- transformers
- quantization
- dq4
---

# MiniMax-M2.5_dq4

This model is a DQ4 quantized version of the original model [MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5).
It was quantized locally using the `mlx_lm` library.

## Quantization Methodology (DQ4)

This model was quantized using the dynamic **DQ4** (4-bit / 5-bit / 6-bit / 8-bit mixed) approach, inspired by the methodology described in the [mlx-community/Kimi-K2.5-mlx-DQ3_K_M-q8](https://huggingface.co/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 4-bit.
- The first 5 layers are kept at higher quality (6-bit).
- Every 5th layer is medium quality (5-bit).
- All other layers (e.g. attention, normalization) remain at 8-bit to serve as the "8-bit brain".