--- license: apache-2.0 language: - en --- ## StripedHyena-Nous-7B (SH-7B)

### About One of the focus areas at Together Research is new architectures for long context, improved training, and inference performance over the Transformer architecture. Spinning out of a research program from our team and academic collaborators, with roots in **signal processing-inspired sequence models**, we are excited to introduce the **StripedHyena** models. StripedHyena is the **first alternative model competitive with the best open-source Transformers** of similar sizes in short and long-context evaluations. **StripedHyena-Nous-7B (SH 7B)** is our **chat model** for this release, and was developed with our collaborators at [Nous Research](https://nousresearch.com/). - Read more here in [our blog](https://www.together.ai/blog/stripedhyena-7b). - Play with the model on our playground! - Dive into the details of our [standalone implementation](https://github.com/togethercomputer/stripedhyena), and our related research: [1](https://arxiv.org/abs/2302.10866), [2](https://arxiv.org/abs/2310.18780), [3](https://arxiv.org/abs/2311.05908). ### Model Architecture StripedHyena is a hybrid architecture composed of multi-head, grouped-query attention and gated convolutions arranged in [Hyena](https://arxiv.org/abs/2302.10866) blocks, different from traditional decoder-only Transformers. - Costant memory decoding in Hyena blocks via representation of convolutions as state-space models (modal or canonical form), or as truncated filters. - Low latency, faster decoding and higher throughput than Transformers. - Improvement to training and inference-optimal scaling laws, compared to optimized Transformer architectures such as Llama-2. - Trained on sequences of up to 32k, allowing it to process longer prompts.