Update README.md

README.md

@@ -6,4 +6,24 @@ base_model:
 - meta-llama/Meta-Llama-3.1-8B
 ---
 
-# TODO documentation about the model
+# Empathetic teacher model
+
+## Overview
+
+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), 
+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. 
+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 
+that facilitates the fine-tuning of various well-known LLMs on custom data. 
+Parameter-efficient fine-tuning is achieved via the QLoRA method [Dettmers et al., 2023](https://proceedings.neurips.cc/paper_files/paper/2023/file/1feb87871436031bdc0f2beaa62a049b-Paper-Conference.pdf).