chore: import upstream snapshot with attribution

docs/features/speculative_decoding/mtp.md

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+# 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.
+
+## Gemma 4 Assistant Models
+
+Gemma 4 assistant checkpoints use vLLM's Gemma 4 MTP path. They are not generic
+draft models, even though they are passed through the `model` field in
+`--speculative-config`.
+
+Use `"method": "mtp"` when serving Gemma 4 with an assistant checkpoint:
+
+```bash
+vllm serve google/gemma-4-E2B-it \
+    --tensor-parallel-size 1 \
+    --max-model-len 8192 \
+    --speculative-config '{"method":"mtp","model":"gg-hf-am/gemma-4-E2B-it-assistant","num_speculative_tokens":1}'
+```
+
+The E2B, E4B, 12B, 26B-A4B, and 31B Gemma 4 IT assistant checkpoints are supported.
+Tower-based variants use `model_type: gemma4_assistant` and the encoder-free
+Gemma 4 Unified variant (12B) uses `model_type: gemma4_unified_assistant`.
+vLLM maps both to `Gemma4MTPModel` internally and wires the assistant layers
+to share KV cache with the target model.
+
+If an older vLLM release logs `SpeculativeConfig(method='draft_model', ...)`
+for a Gemma 4 assistant checkpoint, that release is treating the assistant as a
+generic draft model and may fail during initialization for multimodal Gemma 4
+targets. Upgrade to a version with Gemma 4 MTP support instead.
+
+## 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.