MiniMax-M2-BF16-W4A16

What this model is

• Base model: MiniMaxAI/MiniMax-M2
• Quantization pipeline: llm-compressor
• Quantization recipe: AWQModifier
• Scheme: W4A16
• Main quantized targets: MoE expert MLP weights (w1, w2, w3)
• MoE gates and lm_head are excluded from quantization per recipe

How it was generated

1. Prepare environment and install llm-compressor dependencies.
2. Set model_id in examples/quantizing_moe/minimax_m2_example.py to the BF16 base checkpoint path.
3. Run the example script:
   • python examples/quantizing_moe/minimax_m2_example.py
4. The script applies AWQModifier with W4A16 on MiniMax M2 MoE experts (w1/w2/w3) and saves the compressed checkpoint.
5. Output directory is created as:
   • MiniMax-M2-BF16-W4A16

Notes

• This is a derived quantized artifact, not an official upstream release from MiniMaxAI.
• Inference quality/performance may differ from the original BF16 checkpoint depending on workload and hardware.