metadata
pipeline_tag: text-generation
license: other
license_name: modified-mit
license_link: https://github.com/MiniMax-AI/MiniMax-M2.7/blob/main/LICENSE
library_name: mlx
tags:
- mlx
base_model: MiniMaxAI/MiniMax-M2.7
MiniMax-M2.7 optimized for MLX.
- A mixed-precision quant that balances speed, memory, and accuracy.
- 4 bit baseline with important layers at 5, 6, 8, and BF16.
Usage
# Start server at http://localhost:8080/chat/completions
uvx --from mlx-lm mlx_lm.server \
--host 127.0.0.1 \
--port 8080 \
--model spicyneuron/MiniMax-M2.7-MLX-4.9bit
Benchmarks
| metric | mlx-community_MiniMax-M2.7-4bit | baa-ai_MiniMax-M2.7-RAM-155GB-MLX | 4.9 bit (this model) |
|---|---|---|---|
| bpw | 4.501 | 5.4278 | 4.915 |
| peak memory (1024/512) | 129.632 | 156.051 | 141.458 |
| prompt tok/s (1024) | 739.996 ± 1.565 | 708.147 ± 0.818 | 723.742 ± 0.880 |
| gen tok/s (512) | 48.703 ± 0.116 | 40.253 ± 0.077 | 42.270 ± 0.143 |
| perplexity | 9.120 ± 0.047 | 8.835 ± 0.045 | 4.590 ± 0.027 |
| hellaswag | 0.504 ± 0.011 | 0.509 ± 0.011 | 0.512 ± 0.011 |
| piqa | 0.786 ± 0.01 | 0.787 ± 0.01 | 0.791 ± 0.009 |
| winogrande | 0.636 ± 0.014 | 0.661 ± 0.013 | 0.666 ± 0.013 |
Tested on a Mac Studio M3 Ultra with:
mlx_lm.perplexity --sequence-length 2048 --seed 123
mlx_lm.benchmark --prompt-tokens 1024 --generation-tokens 512 --num-trials 5
mlx_lm.evaluate --tasks hellaswag --seed 123 --num-shots 0 --limit 2000
mlx_lm.evaluate --tasks piqa --seed 123 --num-shots 0 --limit 2000
mlx_lm.evaluate --tasks winogrande --seed 123 --num-shots 0 --limit 2000
Methodology
Quantized with a mlx-lm fork, drawing inspiration from Unsloth/AesSedai/ubergarm style mixed-precision GGUFs. MLX quantization options differ than llama.cpp, but the principles are the same:
- Sensitive layers like MoE routing, attention, and output embeddings get higher precision
- More tolerant layers like MoE experts get lower precision