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inference-optimization/Qwen3-8B-speculators.peagle-qwen3arch-ckpt4

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face
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Model Card

Qwen3-8B P-EAGLE Drafter (Qwen3 Architecture)

P-EAGLE (Parallel Eagle) speculative decoding drafter for Qwen/Qwen3-8B using the Qwen3 draft architecture (--draft-arch qwen3).

The Qwen3 architecture uses additional q_norm/k_norm layers in attention, which stabilize training at higher learning rates compared to the default Llama draft architecture. See speculators#563 for the RFC and experimental results.

Benchmark Results

Evaluated on RedHatAI/speculator_benchmarks (9 subsets) via vLLM throughput mode.

Acceptance lengths

Subsetk=5k=7
HumanEval3.513.73
Math Reasoning3.734.04
QA2.813.01
Question2.993.27
RAG3.062.98
Summarization2.722.55
Tool Call2.712.97
Translation2.802.81
Writing3.003.37
Average3.043.19

Comparison vs Llama-arch baseline

Format: delta vs Llama-arch P-EAGLE.

Subsetk=5k=7
HumanEval+0.50+0.23
Math Reasoning+0.46+0.22
QA+0.41+0.19
Question+0.36+0.24
RAG+0.57+0.05
Summarization+0.69+0.07
Tool Call+0.26+0.10
Translation+0.54-0.07
Writing+0.37+0.31
Average+0.46 (+18%)+0.15 (+5%)

Qwen3-arch outperforms Llama-arch on all 9 subsets at k=5 and 8/9 at k=7.

Training

ParameterValue
Target modelQwen/Qwen3-8B
Draft architectureQwen3 (--draft-arch qwen3)
Learning rate6e-4
Epochs5
Draft vocab size32000
Sequence length8192
Num layers4
Num depths7
Down-sample ratio0.6 (min 0.2)
SchedulerCosine
Hardware2x H200
Training libraryspeculators

Usage

Requires vLLM with Qwen3 Eagle3/P-EAGLE support (vllm#43132) and the architecture resolution fix.
bash
vllm serve Qwen/Qwen3-8B \
  --speculative-config '{
    "model": "inference-optimization/Qwen3-8B-speculators.peagle-qwen3arch-ckpt4",
    "num_speculative_tokens": 7,
    "method": "eagle3",
    "parallel_drafting": true
  }'

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