Files
vllm-project--vllm/docs/features/speculative_decoding/n_gram.md
T
wehub-resource-sync 7ce4c8e27e
pre-commit / pre-run-check (push) Has been cancelled
pre-commit / pre-commit (push) Has been cancelled
chore: import upstream snapshot with attribution
2026-07-13 12:55:37 +08:00

28 lines
803 B
Markdown

# 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}")
```