lmsys/SGLang-EAGLE3-Qwen3-Coder-480B-A35B-Instruct-SpecForge-EigenAI

EAGLE3 For Qwen/Qwen3-Coder-480B-A35B-Instruct
About
SpecBundle is an open-source initiative, jointly driven by the community and industry, to democratize speculative decoding by providing high-performance speculative decoding draft weights for mainstream open-source models.
This checkpoint was trained by the **EigenAI** Team and released as the phase 1 of SpecBundle release. We regenerated the responses in the OpenCoder-LLM/opc-sft-stage1 and trained the model on 1.4M data samples for 2 epochs. This checkpoint was trained using the **SpecForge** framework.
Usage
You can use this checkpoint with the command below.
export SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1
python3 -m sglang.launch_server \
--model Qwen/Qwen3-Coder-480B-A35B-Instruct \
--speculative-algorithm EAGLE3 \
--speculative-draft-model-path lmsys/SGLang-EAGLE3-Qwen3-Coder-480B-A35B-Instruct-perfect-blend-regenerated \
--speculative-num-steps 3 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 4 \
--tp 8Performance
This checkpoint exhibits superior performance on various benchmarks.
<table> <tr> <th>Throughput</th> <th>Acceptance Length</th> </tr> <tr> <td><img src="https://cdn-uploads.huggingface.co/production/uploads/63993d721fad4d6eb265d999/Ekvi9tOqwY2xU08640fG.png" /></td> <td><img src="https://cdn-uploads.huggingface.co/production/uploads/63993d721fad4d6eb265d999/kpJnUyyY5xg7HYVYBhhE.png" /></td> </tr> </table>
You can reproduce the performance with the command below:
# clone specforge
git clone https://github.com/sgl-project/SpecForge.git
cd SpecForge/benchmarks
# run benchmarks
python bench_eagle3.py \
--model Qwen/Qwen3-Coder-480B-A35B-Instruct \
--speculative-algorithm EAGLE3 \
--speculative-draft-model-path lmsys/SGLang-EAGLE3-Qwen3-Coder-480B-A35B-Instruct-perfect-blend-regenerated \
--port 30003 \
--config-list 1,3,1,4 1,5,1,6 1,5,3,6 1,7,1,8 1,7,4,8 \
--benchmark-list humaneval livecodebench \
--dtype bfloat16 \
--tp 8 \
--name qwen3-coder-480b-a35b-spec-bundleAcknowledgement
We sincerely appreciate the collective efforts from both the developers in the open-source community and our industrial partners, especially Ant Group AQ Team, Meituan, Nex-AGI (Qiji Zhifeng), EigenAI for their invaluable contributions to the release of SpecBundle Phase 1.
