MLX 4-bit quantized version of
Akicou/MiniMax-M2-5-REAP-19 for efficient local inference on Apple Silicon.
1from mlx_lm import load, generate
2
3model, tokenizer = load("shieldstackllc/MiniMax-M2-5-REAP-19-mlx-4bit")
4response = generate(model, tokenizer, prompt="Hello!", verbose=True)
Or with
vMLX for native macOS inference.
MiniMax-M2.5 is a large Mixture-of-Experts language model by MiniMax AI. This variant was pruned to 19% fewer experts by
Akicou using REAP (Router Expert Activation Pruning), reducing model size and memory footprint while maintaining strong performance. MLX quantization by
vMLX.
This model was converted and optimized for
vMLX — a free, open source macOS native MLX inference engine for Apple Silicon. Download vMLX to run this model locally with zero configuration.
For questions, issues, or collaboration:
admin@vmlx.net
