ahoybrotherbear/MiniMax-M2.5-3bit-MLX

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 version of MiniMaxAI/MiniMax-M2.5, converted using 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 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 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

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

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 (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. See the original model card for full details on capabilities, benchmarks, and license terms.