MiniMax-M2-3bit-DWQ / README.md
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metadata
pipeline_tag: text-generation
license: mit
library_name: mlx
base_model: MiniMaxAI/MiniMax-M2
tags:
  - mlx

catalystsec/MiniMax-M2-3bit-DWQ

This model was quantized to 3-bit using DWQ with mlx-lm version 0.28.4.

Parameter Value
DWQ learning rate 3e-7
Batch size 1
Dataset allenai/tulu-3-sft-mixture
Initial validation loss 0.146
Final validation loss 0.088
Relative KL reduction ≈40 %
Tokens processed ≈1.09 M

MMLU-PRO Benchmark

Model Score
3-bit DWQ 66.1
3-bit 62.0
MMLU-Pro Benchmark

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("catalystsec/MiniMax-M2-3bit-DWQ")
prompt = "hello"

if tokenizer.chat_template is not None:
    prompt = tokenizer.apply_chat_template(
        [{"role": "user", "content": prompt}],
        add_generation_prompt=True,
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
print(response)