# 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}")
```