chore: update readme

README.md

@@ -4,12 +4,27 @@ language:
 - en
 ---
 
-## StripedHyena-Hessian-7B (SH-7B)
+## StripedHyena-Nous-7B (SH-7B)
 
+<p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/62a1306bbe7fa896d2c8de44/Bfjh77emDsWOY-VmfvU9C.png" width="30%" />
+</p>
+
+### 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.
-  - Lower latency to preprocess long prompts.
-  - Improvements to training and inference compute-optimal scaling laws, compared to Transformers.
+  - 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.
+