OLMoE-1B-7B-Eagle3 Draft Model

📦 Included Files

• pytorch_model.bin: Trained EAGLE Draft model weights
• config.json: Model configuration file (OLMoE architecture)
• tokenizer_config.json: Tokenizer configuration file
• modeling_olmoe_kv.py: OLMoE-specific model code (required for EAGLE inference)
• eagle_data.json: Training dataset (ShareGPT questions + OLMoE-generated answers)
• .gitattributes: Git LFS settings, etc.

🦅 What is the EAGLE Draft Model?

• Fully compatible with OLMoE-1B-7B-0125-Instruct architecture
• The EAGLE Draft layer is structurally similar to the main model’s decoder
• During inference, the draft layer generates multiple tokens in advance, which are then verified/accepted by the main model

📝 Training Data Description

• eagle_data.json
  • Only questions (prompts) are extracted from the ShareGPT dataset
  • For each question, the allenai/OLMoE-1B-7B-0125-Instruct model generates its own answer
  • Thus, the model’s self-generated answers are used as ground truth to train the draft layer
  • This approach ensures the draft layer learns a distribution very close to the main model’s decoder,
    maximizing EAGLE inference performance

🛠️ Usage

1. Using Model Weights/Config Files

• pytorch_model.bin, config.json, and tokenizer_config.json
  can be used directly with HuggingFace Transformers or EAGLE code.

2. Integrating with EAGLE Inference Code

• Copy modeling_olmoe_kv.py
  into the official EAGLE repo at EAGLE/eagle/model/.
• In your EAGLE inference script, import as:
  from eagle.model.modeling_olmoe_kv import OlmoeForCausalLM

3. Example Code

⚠️ Notes

• eagle_data.json contains only OLMoE-generated answers for public ShareGPT questions.
• The EAGLE Draft layer should be designed as close as possible to the main model’s decoder
  for optimal inference efficiency.
• modeling_olmoe_kv.py must be included in your EAGLE inference code for correct operation.

📚 References

• EAGLE: Fast Decoding for Large Language Models
• allenai/OLMoE-1B-7B-0125-Instruct
• ShareGPT Dataset