Downloads · 30 days
36
1% of all-time downloads
MiniMax-M2.7-MLX-4.9bit
spicyneuron/MiniMax-M2.7-MLX-4.9bit
MiniMax-M2.7-MLX-4.9bit is a text generation model from spicyneuron. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as other.
All-time downloads
2.4K
Public
Parameters
229B
141 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors141 GB · 100%
Parameter types
How the weights are stored.
U32229B · 100%
Try a prompt
Base models
Model card
From the Hugging Face model README
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
- Task
- Text Generation
- Library
- mlx
- Type
- minimax_m2
- License
- other
- Created
- Apr 12, 2026
- Updated
- Apr 21, 2026