chore: more info in the readme

README.md

@@ -6,10 +6,18 @@ language:
 
 ## StripedHyena-Hessian-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.
+
+- Read more here in [our blog](https://together-ai.webflow.io/blog/stripedhyena-7b)
+- Play with the model on our playground!
+- Dive into the details of our [Standalone implementation](https://github.com/togethercomputer/stripedhyena)
 
 ### 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 Transformers.
+  - Trained on sequences of up to 32k, allowing it to process longer prompts.