Update README.md

README.md

@@ -42,6 +42,7 @@ To enhance the long-context ability, we exclude data shorter than 2K word. The i
 - We then fine-tune the model to focus on its few shot capacity under long context, including 20% Natural Instructions (NI), 20% Public Pool of Prompts (P3), 20% the Pile. We decontaminated all data against HELM core scenarios . We teach the model to leverage the in-context examples by packing examples into one 32K-token sequence. To maintain the knowledge learned from the first piece of data, we incorporate 20% RedPajama-Data Book and 20% RedPajama-Data ArXiv.
 
 Next, we provide examples of how to fine-tune the model for specific applications. 
+The example datasets are placed in [togethercomputer/Long-Data-Collections](https://huggingface.co/datasets/togethercomputer/Long-Data-Collections)
 You can use the [OpenChatKit](https://github.com/togethercomputer/OpenChatKit) to fine-tune your own 32K model over Llama-2-7B-32K-beta.
 Please refer to [OpenChatKit](https://github.com/togethercomputer/OpenChatKit) for step-by-step illustrations.