# MiniMax-M2 Model Repository

This is the official MiniMax-M2 model repository containing a 230B parameter MoE model with 10B active parameters, optimized for coding and agentic workflows.

## Model Information

- **Model Type**: Mixture of Experts (MoE)
- **Total Parameters**: 230B
- **Active Parameters**: 10B
- **Architecture**: Transformer-based MoE
- **License**: Modified MIT
- **Pipeline Tag**: text-generation

## Usage

This model can be used with various inference frameworks:

### Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("your-username/MiniMax-M2")
tokenizer = AutoTokenizer.from_pretrained("your-username/MiniMax-M2")
```

### vLLM
```python
from vllm import LLM, SamplingParams

llm = LLM(model="your-username/MiniMax-M2")
```

### SGLang
```python
from sglang import function, system, user, assistant, gen, select

@function
def multi_turn_question(s, question):
    s += system("You are a helpful assistant.")
    s += user(question)
    s += assistant(gen("answer", max_tokens=256))
    return s["answer"]
```

## Model Details

- **Context Length**: 128K tokens
- **Thinking Format**: Uses `<think>...</think>` tags for reasoning
- **Recommended Parameters**:
  - Temperature: 1.0
  - Top-p: 0.95
  - Top-k: 40

## Deployment Guides

See the `docs/` directory for detailed deployment guides:
- [Transformers Guide](docs/transformers_deploy_guide.md)
- [vLLM Guide](docs/vllm_deploy_guide.md)
- [SGLang Guide](docs/sglang_deploy_guide.md)
- [MLX Guide](docs/mlx_deploy_guide.md)

## License

This model is released under the Modified MIT License. See the [license file](https://github.com/MiniMax-AI/MiniMax-M2/blob/main/LICENSE) for details.