chore: update readme

README.md

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 ## StripedHyena-Hessian-7B (SH-7B)
 
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+  <img src="https://cdn-uploads.huggingface.co/production/uploads/62a1306bbe7fa896d2c8de44/Bfjh77emDsWOY-VmfvU9C.png" width="30%" />
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 ### 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.
+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)
+- 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)
+- 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 Transformers.
+  - Improvement to training and inference-optimal scaling laws, compared to optimized Transformer architectures such as Llama.
   - Trained on sequences of up to 32k, allowing it to process longer prompts.