StripedHyena-Hessian-7B
| Property | Value |
|---|---|
| Model Size | 7B parameters |
| Author | Together Computer |
| Context Length | 32,000 tokens |
| Architecture | Hybrid (Attention + Convolution) |
| Model URL | HuggingFace |
What is StripedHyena-Hessian-7B?
StripedHyena-Hessian-7B (SH 7B) is an innovative language model that breaks away from traditional Transformer architecture, introducing a hybrid approach that combines multi-head, grouped-query attention with gated convolutions arranged in Hyena blocks. This model represents a significant advancement in AI architecture design, offering competitive performance with leading open-source Transformers while providing improved efficiency and longer context handling.
Implementation Details
The model employs a sophisticated architecture that leverages constant memory decoding in Hyena blocks through state-space model representations. It's designed with mixed precision requirements, particularly maintaining float32 precision for poles and residues during long-prompt processing or training.
- Hybrid architecture combining attention and convolution mechanisms
- Constant memory decoding via state-space model representations
- Optimized for both training and inference scaling
- Support for sequences up to 32k tokens
Core Capabilities
- Low latency and faster decoding compared to traditional Transformers
- Higher throughput than conventional architectures
- Improved training and inference-optimal scaling laws vs Llama-2
- Long-context processing with 32k token support
- Competitive performance in both short and long-context evaluations
Frequently Asked Questions
Q: What makes this model unique?
StripedHyena-Hessian-7B stands out for its hybrid architecture that moves beyond traditional Transformers, offering competitive performance while maintaining better efficiency through its innovative combination of attention mechanisms and convolutions.
Q: What are the recommended use cases?
The model is particularly well-suited for applications requiring long context processing (up to 32k tokens), high-throughput scenarios, and cases where efficient inference is crucial. It's designed to handle both short and long-context evaluations effectively.
