LLaMA-160M Accelerator
| Property | Value |
|---|---|
| Parameter Count | 199M |
| Model Type | Transformer Accelerator |
| License | Apache 2.0 |
| Tensor Type | FP16 |
What is llama-160m-accelerator?
The llama-160m-accelerator is a specialized model designed to enhance the inference speed of the base LLaMA-160M model. It implements an innovative multi-stage MLP architecture inspired by the Medusa speculative decoding framework, focusing on accelerating text generation while maintaining quality.
Implementation Details
This accelerator transforms the traditional MLP into a multi-stage system where each stage predicts subsequent tokens based on both state vectors and previously sampled tokens. The architecture leverages paged attention KV-cache and speculator mechanisms to optimize performance.
- Multi-stage MLP architecture for token prediction
- Integration with vLLM for testing and deployment
- Lightweight training process (complete in days)
- Compatible with production server environments
Core Capabilities
- Speculative decoding for faster inference
- State vector-based contextual processing
- High-quality draft n-gram generation
- Seamless integration with existing LLaMA infrastructure
Frequently Asked Questions
Q: What makes this model unique?
This model's unique feature is its specialized architecture for accelerating the base LLaMA-160M model through multi-stage MLP prediction, making it particularly effective for production deployment scenarios requiring faster inference.
Q: What are the recommended use cases?
The model is best suited for applications requiring rapid text generation, particularly in production environments using the IBM Production TGIS or Hugging Face TGI frameworks. It's especially valuable in scenarios where inference speed is crucial while maintaining generation quality.
