inference-optimization/Qwen3-8B-from-Qwen3-8B_regen-speculators.eagle3-qwen3arch-ckpt1

🤗 Hugging Face 来源apache-2.01B 参数2.0 GBsafetensors✓ 1 个校验和今天更新
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curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo inference-optimization/Qwen3-8B-from-Qwen3-8B_regen-speculators.eagle3-qwen3arch-ckpt1 ./model-folder
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Qwen3-8B Eagle3 Drafter (Qwen3 Architecture)

Eagle3 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 with sampling params temp=0.6, top_p=0.95, top_k=20.

Acceptance lengths

Use Case k=1 k=2 k=3 k=4 k=5 k=6 k=7
Coding (HumanEval, 164 samples) 1.81 2.40 2.80 3.06 3.22 3.30 3.33
Math Reasoning (gsm8k, 80 samples) 1.83 2.50 2.95 3.28 3.45 3.61 3.68
Text Summarization (CNN/DM, 80 samples) 1.69 2.11 2.35 2.46 2.50 2.52 2.52

Comparison vs Llama-arch baseline

Trained on the same dataset with the same hyperparameters (except LR). Format: delta vs llamaarch-ckpt1.

Use Case k=1 k=2 k=3 k=4 k=5 k=6 k=7
Coding +0.02 +0.05 +0.09 +0.18 +0.22 +0.24 +0.23
Math Reasoning +0.02 +0.08 +0.08 +0.16 +0.15 +0.19 +0.23
Text Summarization +0.02 +0.06 +0.10 +0.13 +0.13 +0.14 +0.13

Outperforms the Llama-arch baseline by ~3-7% across all benchmarks and all k values, with the gap widening at higher k (math k=7: 3.68 vs 3.45, +6.7%).

Training

Parameter Value
Target model Qwen/Qwen3-8B
Draft architecture Qwen3 (--draft-arch qwen3)
Learning rate 5e-4
Epochs 2
Draft vocab size 32000
Sequence length 8192
Training mode Online (hidden states generated on-the-fly)
Dataset inference-optimization/Qwen3-8B-Regenerated-Collection (magpie + ultrachat subsets, 508k samples)
Hardware 7x H200 (2 vLLM DP=2, 5 training FSDP)
Training library speculators

Usage

Requires vLLM with Qwen3 Eagle3 support (vllm#43132) and the architecture resolution fix.

vllm serve Qwen/Qwen3-8B \
  --speculative-config '{
    "model": "inference-optimization/Qwen3-8B-from-Qwen3-8B_regen-speculators.eagle3-qwen3arch-ckpt1",
    "num_speculative_tokens": 3,
    "method": "eagle3"
  }'

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