metadata
language: en
license: mit
library_name: mlx
pipeline_tag: text-generation
tags:
- transformers
- mlx
base_model:
- MiniMaxAI/MiniMax-M2
mlx-community/MiniMax-M2-mlx-8bit-gs32
This model mlx-community/MiniMax-M2-mlx-8bit-gs32 was converted to MLX format from MiniMaxAI/MiniMax-M2 using mlx-lm version 0.28.1.
Recipe:
- 8-bit
- group-size 32
- 9 bits per weight (bpw)
You can find more similar MLX model quants for a single Apple Mac Studio M3 Ultra with 512 GB at https://huggingface.co/bibproj
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/MiniMax-M2-mlx-8bit-gs32")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)