SGLang-EAGLE3-Qwen3-Coder-30B-A3B-Instruct

This is an EAGLE3 draft model for speculative decoding with Qwen/Qwen3-Coder-30B-A3B-Instruct.

Model Description

EAGLE3 (Efficient Auto-regressive Language model Generation with Learned Embeddings) is a speculative decoding technique that uses a lightweight draft model to predict future tokens, which are then verified by the target model in parallel. This can significantly accelerate inference speed (2-3x) without any loss in output quality.

Key Features

• Target Model: Qwen3-Coder-30B-A3B-Instruct (30B parameters, 3B active)
• Draft Model Size: ~350MB (single transformer layer)
• Training Data: OpenPromptContainer (OPC) regenerated dataset
• Training Steps: 295,000 (Epoch 1)
• Framework: Trained with SpecForge

Training Metrics

Metric	Value
First Token Accuracy (acc_0)	88.19%
Average Accuracy (7 positions)	85.19%
Training Epochs	1+ (295k steps)

Usage

With SGLang

import sglang as sgl

# Launch with EAGLE3 speculative decoding
llm = sgl.Engine(
    model_path="Qwen/Qwen3-Coder-30B-A3B-Instruct",
    speculative_algorithm="EAGLE",
    speculative_draft_model_path="sgl-project/SGLang-EAGLE3-Qwen3-Coder-30B-A3B-Instruct",
    speculative_num_steps=5,
    speculative_eagle_topk=8,
    speculative_num_draft_tokens=64,
)

# Generate text
output = llm.generate("Write a Python function to sort a list:")
print(output)

With SGLang Server

python -m sglang.launch_server \
    --model-path Qwen/Qwen3-Coder-30B-A3B-Instruct \
    --speculative-algorithm EAGLE \
    --speculative-draft-model-path sgl-project/SGLang-EAGLE3-Qwen3-Coder-30B-A3B-Instruct \
    --speculative-num-steps 5 \
    --speculative-eagle-topk 8 \
    --speculative-num-draft-tokens 64 \
    --tp 8

Model Architecture

The EAGLE3 draft model is a lightweight transformer that:

• Shares embeddings with the target model
• Uses a single transformer layer (hidden_size=2048, intermediate_size=12288)
• Predicts multiple future tokens autoregressively
• Uses the target model's hidden states as input

{
  "architectures": ["LlamaForCausalLMEagle3"],
  "hidden_size": 2048,
  "intermediate_size": 12288,
  "num_attention_heads": 32,
  "num_key_value_heads": 4,
  "num_hidden_layers": 1,
  "vocab_size": 151936
}

Training Details

• Framework: SpecForge with SGLang backend
• Hardware: 4x NVIDIA H200 GPUs (TP=4)
• Batch Size: 1 per GPU
• Learning Rate: 1e-4 with cosine annealing
• Max Sequence Length: 4096
• Attention Backend: FlexAttention

Citation

If you use this model, please cite:

@article{li2024eagle,
  title={EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty},
  author={Li, Yuhui and Wei, Fangyun and Zhang, Chao and Zhang, Hongyang},
  journal={arXiv preprint arXiv:2401.15077},
  year={2024}
}

@misc{sglang2024,
  title={SGLang: Efficient Execution of Structured Language Model Programs},
  author={Zheng, Lianmin and others},
  year={2024},
  url={https://github.com/sgl-project/sglang}
}

License

This model is released under the Apache 2.0 License, following the base model's license.