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README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
+tags:
+  - eagle
+  - eagle3
+  - speculative-decoding
+  - draft-model
+  - sglang
+  - qwen3
+  - code
+language:
+  - en
+  - zh
+pipeline_tag: text-generation
+---
+
+# SGLang-EAGLE3-Qwen3-Coder-30B-A3B-Instruct
+
+This is an **EAGLE3 draft model** for speculative decoding with [Qwen/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/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](https://github.com/sgl-project/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
+
+```python
+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
+
+```bash
+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
+
+```json
+{
+  "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:
+
+```bibtex
+@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.

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLMEagle3"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "draft_vocab_size": 32000,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "max_position_embeddings": 2048,
+  "max_window_layers": 48,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 1,
+  "num_key_value_heads": 4,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "pad_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_p": 0.8,
+  "top_k": 20
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d12a7f2411b7282d7b33b3e475c28df63c2af586420a00d1065215b946300dcc
+size 366378848

tokenizer_config.json

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+    "chat_template": "{% macro render_extra_keys(json_dict, handled_keys) %}\n    {%- if json_dict is mapping %}\n        {%- for json_key in json_dict if json_key not in handled_keys %}\n            {%- if json_dict[json_key] is mapping or (json_dict[json_key] is sequence and json_dict[json_key] is not string) %}\n                {{- '\\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson | safe) ~ '</' ~ json_key ~ '>' }}\n            {%- else %}\n                {{-'\\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '</' ~ json_key ~ '>' }}\n            {%- endif %}\n        {%- endfor %}\n    {%- endif %}\n{% endmacro %}\n\n{%- if messages[0][\"role\"] == \"system\" %}\n    {%- set system_message = messages[0][\"content\"] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n\n{%- if not tools is defined %}\n    {%- set tools = [] %}\n{%- endif %}\n\n{%- if system_message is defined %}\n    {{- 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+}