Update README.md

README.md

@@ -69,7 +69,8 @@ All smaller DPO'd models have strong performance per model size in the category
 ## Intended uses & limitations
 
 The model was initially fine-tuned on a filtered and preprocessed of the Tulu V2 mix dataset (TODO add link), which contains a diverse range of human created instructions and synthetic dialogues generated primarily by other LLMs. 
-We then further aligned the model with a [Jax DPO trainer](https://github.com/hamishivi/EasyLM/blob/main/EasyLM/models/llama/llama_train_dpo.py) built on [EasyLM](https://github.com/young-geng/EasyLM) on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. 
+We then further aligned the model with a [Jax DPO trainer](https://github.com/hamishivi/EasyLM/blob/main/EasyLM/models/llama/llama_train_dpo.py) built on [EasyLM](https://github.com/young-geng/EasyLM) on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contains 64k prompts and model completions that are ranked by GPT-4. 
+
 
 <!--  You can find the datasets used for training Tulu V2 [here]() -->