---
base_model: MiniMaxAI/MiniMax-M2.5
library_name: mlx
tags:
  - mlx
  - quantized
  - 3bit
  - minimax_m2
  - text-generation
  - conversational
  - apple-silicon
license: other
license_name: modified-mit
license_link: https://huggingface.co/MiniMaxAI/MiniMax-M2.5/blob/main/LICENSE
pipeline_tag: text-generation
---

# MiniMax-M2.5 3-bit MLX

**⚠️ UPLOAD IN PROGRESS -- model files still uploading, not yet ready for use.**

This is a 3-bit quantized [MLX](https://github.com/ml-explore/mlx) version of [MiniMaxAI/MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5), converted using [mlx-lm](https://github.com/ml-explore/mlx-lm) v0.30.7.

MiniMax-M2.5 is a 229B parameter Mixture of Experts model (10B active parameters) that achieves 80.2% on SWE-Bench Verified and is SOTA in coding, agentic tool use, and search tasks.

## Important: Quality Note

**This is an aggressive quantization.** Independent testing by [inferencerlabs](https://huggingface.co/inferencerlabs/MiniMax-M2.5-MLX-9bit) shows significant quality degradation below 4 bits for this model (q3.5 scored 43% token accuracy vs 91%+ at q4.5). This 3-bit quant was manually tested on coding and reasoning tasks and produced coherent output, but expect noticeable quality loss compared to 4-bit and above.

**If you have 256GB+ of RAM, use the [4-bit quant](https://huggingface.co/mlx-community/MiniMax-M2.5-4bit) instead.** This 3-bit version is primarily useful for machines with 192GB of unified memory where the 4-bit version won't fit.

## Requirements

- Apple Silicon Mac (M2 Ultra or later)
- At least 192GB of unified memory

## Quick Start

Install mlx-lm:

```
pip install -U mlx-lm
```

### CLI

```bash
mlx_lm.generate \
  --model ahoybrotherbear/MiniMax-M2.5-3bit-MLX \
  --prompt "Hello, how are you?" \
  --max-tokens 256 \
  --temp 0.7
```

### Python

```python
from mlx_lm import load, generate

model, tokenizer = load("ahoybrotherbear/MiniMax-M2.5-3bit-MLX")

messages = [{"role": "user", "content": "Hello, how are you?"}]
prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)

response = generate(
    model, tokenizer,
    prompt=prompt,
    max_tokens=256,
    temp=0.7,
    verbose=True
)
print(response)
```

## Conversion Details

- **Source model**: [MiniMaxAI/MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5) (FP8)
- **Converted with**: mlx-lm v0.30.7
- **Quantization**: 3-bit (3.501 average bits per weight)
- **Original parameters**: 229B total / 10B active (MoE)
- **Peak memory during inference**: ~100GB
- **Generation speed**: ~54 tokens/sec on M3 Ultra

## Original Model

MiniMax-M2.5 was created by [MiniMaxAI](https://huggingface.co/MiniMaxAI). See the [original model card](https://huggingface.co/MiniMaxAI/MiniMax-M2.5) for full details on capabilities, benchmarks, and license terms.