---
license: other
license_name: minimax-community
license_link: LICENSE
library_name: mlx
pipeline_tag: text-generation
base_model: MiniMaxAI/MiniMax-M3.1-Flash
tags:
- moe
- agent
- coding
- mlx
---

# mlx-community/MiniMax-M3.1-Flash-Text-4bit

This model [mlx-community/MiniMax-M3.1-Flash-Text-4bit](https://huggingface.co/mlx-community/MiniMax-M3.1-Flash-Text-4bit) was converted to MLX format from [MiniMaxAI/MiniMax-M3.1-Flash](https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash) using mlx-lm with [ml-explore/mlx-lm#2003](https://github.com/ml-explore/mlx-lm/pull/2003). Refer to the [original model card](https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash) for more details on the model.

This is a text-only conversion. The vision tower is not included.

The weights keep the original quantization. The routed experts are NVFP4 with a per-expert global scale, the attention, shared expert and dense MLP layers are MXFP8, and the router stays in float32. Greedy output matched the original checkpoint token for token in testing. Peak memory was about 249 GB in a short generation test.

Tested with mlx-lm 0.32.2.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/MiniMax-M3.1-Flash-Text-4bit")

prompt = "hello"

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

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