chore: import upstream snapshot with attribution

docs/features/speculative_decoding/n_gram.md

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+# N-Gram Speculation
+
+The following code configures vLLM to use speculative decoding where proposals are generated by
+matching n-grams in the prompt. For more information read [this thread.](https://x.com/joao_gante/status/1747322413006643259)
+
+```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="Qwen/Qwen3-8B",
+    tensor_parallel_size=1,
+    speculative_config={
+        "method": "ngram",
+        "num_speculative_tokens": 5,
+        "prompt_lookup_max": 4,
+    },
+)
+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}")
+```