Update README.md (#15)

- Update README.md (94f7ba2d76c1644875a50aedcb774cf16d3ef0ca)


Co-authored-by: Leo Pekelis <leo-pekelis-gradient@users.noreply.huggingface.co>

README.md

@@ -16,7 +16,9 @@ For more info see our [End-to-end development service for custom LLMs and AI sys
 
 This model extends LLama-3 8B's context length from 8k to > 1040K, developed by Gradient, sponsored by compute from [Crusoe Energy](https://huggingface.co/crusoeai). It demonstrates that SOTA LLMs can learn to operate on long context with minimal training by appropriately adjusting RoPE theta. We trained on 830M tokens for this stage, and 1.4B tokens total for all stages, which is < 0.01% of Llama-3's original pre-training data.
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/6585dc9be92bc5f258156bd6/6MKLoX2ruLIaREiyb6coO.png)
+**Update (5/3): We further fine-tuned our model to strengthen its assistant-like chat ability as well. The NIAH result is updated.** 
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6585dc9be92bc5f258156bd6/-qaI__83ksClzoJzlqZjq.png)
 
 **Approach:**
 
@@ -32,7 +34,7 @@ Notably, we layered parallelism on top of Ring Attention with a custom network t
 
 **Data:**
 
-For training data, we generate long contexts by augmenting [SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B).
+For training data, we generate long contexts by augmenting [SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B). We also fine-tune on a chat dataset based on UltraChat [4], following a similar recipe for data augmentation to [2].
 
 **Progressive Training Details:**
 
@@ -100,6 +102,9 @@ Drop an email to [contact@gradient.ai](mailto:contact@gradient.ai)
 
 [3] https://github.com/jzhang38/EasyContext
 
+[3] Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Zhi Zheng, Shengding Hu, Zhiyuan
+Liu, Maosong Sun, and Bowen Zhou. Enhancing chat language models by scaling
+high-quality instructional conversations. arXiv preprint arXiv:2305.14233, 2023.
 
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