Update README.md

README.md

@@ -13,15 +13,14 @@ base_model:
 This is a LLM fine-tuned with real-life, ideally-empathetic teacher-student conversations. 
 This model processes the recent conversation history and provides guidance on how a teacher might respond to the student's utterance.
 
-To fine-tune an open-weighted LLM to act as this generic teacher, we are using the following datasets: 
-the Teacher-Student Chatroom Corpus, 
-TSCCv2 [Caines et al., 2022](https://aclanthology.org/2022.nlp4call-1.3), 
+To fine-tune an open-weighted LLM to act as this generic teacher, we have used the following datasets: 
+the Teacher-Student Chatroom Corpus, TSCCv2 [Caines et al., 2022](https://aclanthology.org/2022.nlp4call-1.3), 
 CIMA [Stasaski et al., 2020](https://aclanthology.org/2020.bea-1.5), 
 the Multicultural Classroom Discourse Dataset [Rapanta et al., 2021](https://www.sciencedirect.com/science/article/pii/S2352340921007940), 
 MathDial [Macina et al., 2023](https://aclanthology.org/2023.findings-emnlp.372), and 
 Conversational Uptake [Demszky et al., 2021].
 
-We are evaluating LLaMa-3, Phi-3, and Gemma-2 for this task. 
+We are evaluating LLaMa-3 for this task. 
 Instead of using programmable fine-tuning libraries such as Axolotl ([link](https://github.com/OpenAccess-AI-Collective/axolotl)) 
 or Huggingface TRL ([link](https://github.com/huggingface/trl)), 
 we are employing the more general command-line LLaMA-Factory ([link](https://github.com/hiyouga/LLaMA-Factory)) toolkit