# MTP (Multi-Token Prediction)

MTP is a speculative decoding method where the target model includes native
multi-token prediction capability. Unlike draft-model-based methods, you do not
need to provide a separate draft model.

MTP is useful when:

- Your model natively supports MTP.
- You want model-based speculative decoding with minimal extra configuration.

## Offline Example

```python
from vllm import LLM, SamplingParams

prompts = ["The future of AI is"]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

llm = LLM(
    model="XiaomiMiMo/MiMo-7B-Base",
    tensor_parallel_size=1,
    speculative_config={
        "method": "mtp",
        "num_speculative_tokens": 1,
    },
)
outputs = llm.generate(prompts, sampling_params)

for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```

## Online Example

```bash
vllm serve XiaomiMiMo/MiMo-7B-Base \
    --tensor-parallel-size 1 \
    --speculative_config '{"method":"mtp","num_speculative_tokens":1}'
```

## Notes

- MTP only works for model families that support MTP in vLLM.
- `num_speculative_tokens` controls speculative depth. A small value like `1`
  is a good default to start with.
- If your model does not support MTP, use another method such as EAGLE or draft
  model speculation.