Update README.md

README.md

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 This model extends LLama-3's context length from 8k to > 130K, developed by Gradient, sponsored by compute from Crusoe Energy. It demonstrates that SOTA LLMs can learn to operate on long context with minimal training (< 200M tokens) by appropriately adjusting RoPE theta. 
 
-Approach:
+**Approach:**
 
 - [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as the base
 - NTK-aware interpolation [1] to initialize an optimal schedule for RoPE theta, followed by a new data-driven RoPE theta optimization technique
 - progressive training on increasing context lengths similar to the [Large World Model](https://huggingface.co/LargeWorldModel) [2]
 
-Infra:
+**Infra:**
 
 We build on top of the EasyContext Blockwise RingAttention library [3] to scalably and efficiently train on contexts up to 256k in length on Crusoe Energy's high performance L40S cluster. 
 
-Data:
+**Data:**
 
 For training data, we generate long contexts by augmenting [SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B).
 
@@ -55,6 +55,7 @@ Gradient is accelerating AI transformation across industries. https://gradient.a
 
 Drop an email to [[email protected]](mailto:[email protected])
 
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 # Base Model