51 lines
1.3 KiB
Markdown
51 lines
1.3 KiB
Markdown
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# MTP (Multi-Token Prediction)
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MTP is a speculative decoding method where the target model includes native
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multi-token prediction capability. Unlike draft-model-based methods, you do not
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need to provide a separate draft model.
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MTP is useful when:
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- Your model natively supports MTP.
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- You want model-based speculative decoding with minimal extra configuration.
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## Offline Example
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```python
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from vllm import LLM, SamplingParams
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prompts = ["The future of AI is"]
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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llm = LLM(
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model="XiaomiMiMo/MiMo-7B-Base",
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tensor_parallel_size=1,
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speculative_config={
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"method": "mtp",
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"num_speculative_tokens": 1,
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},
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)
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outputs = llm.generate(prompts, sampling_params)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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```
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## Online Example
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```bash
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vllm serve XiaomiMiMo/MiMo-7B-Base \
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--tensor-parallel-size 1 \
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--speculative_config '{"method":"mtp","num_speculative_tokens":1}'
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```
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## Notes
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- MTP only works for model families that support MTP in vLLM.
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- `num_speculative_tokens` controls speculative depth. A small value like `1`
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is a good default to start with.
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- If your model does not support MTP, use another method such as EAGLE or draft
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model speculation.
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