---
license: apache-2.0
tags:
- eagle3
- speculative-decoding
- llama4
- vllm
- testing
---

# Llama4 Scout 17B Eagle3 Dummy Drafter

This is a **dummy/test drafter model** for testing the Eagle3 speculative decoding implementation with Llama4 Scout 17B Instruct models in vLLM.

⚠️ **WARNING**: This is not a real model and should not be used for actual inference. It contains random weights and is only for testing purposes.

## Model Details

- **Architecture**: Llama4ForCausalLM (Eagle3 drafter variant)
- **Target Model**: Llama4 Scout 17B Instruct (specifically `RedHatAI/Llama-4-Scout-17B-16E-Instruct-quantized.w4a16`)
- **Base Model**: Based on the Instruct version of Llama4 17B Scout model
- **Hidden Size**: 2048
- **Layers**: 1 (single decoder layer as per Eagle3 design)
- **Vocabulary**: 128256 tokens
- **Parameters**: ~322M

## Configuration

This drafter model is specifically designed for the Instruct version of Llama4 Scout 17B and uses:
- Eagle3 speculative decoding architecture
- Single-layer transformer with auxiliary hidden state combination
- Llama4 layer structure with RoPE (Rotary Position Embedding)
- SGLang-compatible weight naming (midlayer.*)
- Vocabulary mappings (t2d/d2t) for draft-to-target token conversion

## Usage

This model is designed specifically for testing the vLLM Eagle3 implementation:

```python
# Use with vLLM for testing Eagle3 speculative decoding with Llama4 Scout
vllm serve RedHatAI/Llama-4-Scout-17B-16E-Instruct-quantized.w4a16 \
    --speculative-config '{"method": "eagle3", "model": "nm-testing/llama4-scout-17b-eagle3-dummy-drafter", ...}'
```

## Testing Purpose Only

This model:
- Contains random weights
- Is not trained on any data
- Should not be used for actual inference
- Is only for vLLM development and testing

## Related

- vLLM: https://github.com/vllm-project/vllm
- Eagle3: Speculative decoding method