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/chat/completions endpoint.1vllm serve Qwen/Qwen3-8B \
2 -tp 1 \
3 --speculative-config '{
4 "model": "RedHatAI/Qwen3-8B-speculator.eagle3",
5 "num_speculative_tokens": 3,
6 "method": "eagle3"
7 }'| Use Case | Dataset | Number of Samples |
|---|---|---|
| Coding | HumanEval | 168 |
| Math Reasoning | gsm8k | 80 |
| Text Summarization | CNN/Daily Mail | 80 |
| Use Case | k=1 | k=2 | k=3 | k=4 | k=5 | k=6 | k=7 |
|---|---|---|---|---|---|---|---|
| Coding | 1.72 | 2.17 | 2.39 | 2.59 | 2.60 | 2.59 | 2.69 |
| Math Reasoning | 1.73 | 2.20 | 2.48 | 2.63 | 2.72 | 2.79 | 2.81 |
| Text Summarization | 1.62 | 1.96 | 2.13 | 2.24 | 2.25 | 2.29 | 2.30 |



1GUIDELLM__PREFERRED_ROUTE="chat_completions" \
2guidellm benchmark \
3 --target "http://localhost:8000/v1" \
4 --data "RedHatAI/speculator_benchmarks" \
5 --data-args '{"data_files": "HumanEval.jsonl"}' \
6 --rate-type sweep \
7 --max-seconds 600 \
8 --output-path "Qwen3-8B-HumanEval.json" \
9 --backend-args '{"extra_body": {"chat_completions": {"temperature":0.6, "top_p":0.95, "top_k":20}}}'1GUIDELLM__PREFERRED_ROUTE="chat_completions" \
2guidellm benchmark \
3 --target "http://localhost:8000/v1" \
4 --data "RedHatAI/speculator_benchmarks" \
5 --data-args '{"data_files": "HumanEval.jsonl"}' \
6 --profile sweep \
7 --max-seconds 1800 \
8 --output-path "my_output.json" \
9 --backend-args '{"extras": {"body": {"temperature":0.6, "top_p":0.95, "top_k":20}}}'