Update README.md

README.md

@@ -12,7 +12,7 @@ tags:
 license: apache-2.0
 ---
 
-# Model Card for faresfawzi/ToolACE-2-8B-SCRIBE
+# Model Card for faresfawzi/Qwen3-8B-SCRIBE
 
 ## Abstract
 Language models can be used to provide interactive, personalized student feedback in educational settings. However, real-world deployment faces three key challenges: privacy concerns, limited computational resources, and the need for pedagogically valid responses. These constraints require small, open-source models that can run locally and reliably ground their outputs in correct information. We introduce SCRIBE, a framework for multi-hop, tool-augmented reasoning designed to generate valid responses to student questions about feedback reports. SCRIBE combines domain-specific tools with a self-reflective inference pipeline that supports iterative reasoning, tool use, and error recovery. We distil these capabilities into 3B and 8B models via two-stage LoRA fine-tuning on synthetic GPT-4o-generated data. Evaluation with a human-aligned GPT-Judge and a user study with 108 students shows that 8B-SCRIBE models achieve comparable or superior quality to much larger models in key dimensions such as relevance and actionability, while being perceived on par with GPT-4o and Llama-3.3 70B by students. These findings demonstrate the viability of SCRIBE for low-resource, privacy-sensitive educational applications.
@@ -21,8 +21,8 @@ Language models can be used to provide interactive, personalized student feedbac
 
 ## Model Description
 
-**ToolACE-2-8B-SCRIBE** is a fine-tuned large language model for **interactive educational feedback**.  
-It builds on **Team-ACE/ToolACE-2.5-Llama-3.1-8B** and incorporates the **SCRIBE framework**: structured chain reasoning with multi-hop tool calling and self-reflection, enabling small models to deliver **pedagogically valid, actionable, and context-grounded explanations** to student questions.
+**Qwen3-8B-SCRIBE** is a fine-tuned large language model for **interactive educational feedback**.  
+It builds on **Qwen/Qwen3-8B** and incorporates the **SCRIBE framework**: structured chain reasoning with multi-hop tool calling and self-reflection, enabling small models to deliver **pedagogically valid, actionable, and context-grounded explanations** to student questions.
 
 - **Developed by:** EPFL (Machine Learning for Education Lab)  
 - **Paper:** *SCRIBE: Structured Chain Reasoning for Interactive Behavior Explanations using Tool Calling*  
@@ -30,7 +30,7 @@ It builds on **Team-ACE/ToolACE-2.5-Llama-3.1-8B** and incorporates the **SCRIBE
 - **Model type:** Tool-augmented 8B LLM fine-tuned with two-stage LoRA  
 - **Languages:** English  
 - **License:** Apache 2.0  
-- **Finetuned from:** `Team-ACE/ToolACE-2.5-Llama-3.1-8B`  
+- **Finetuned from:** `Qwen/Qwen3-8B`  
 
 ---
 
@@ -71,12 +71,6 @@ The model is designed to:
 
 If you use this model, please cite:
 
-**APA**  
-Fawzi, F., Swamy, V., Glandorf, D., Nazaretsky, T., & Käser, T. (2025).  
-*SCRIBE: Structured Chain Reasoning for Interactive Behavior Explanations using Tool Calling*. EPFL.  
-
-Liu, W., Huang, X., Zeng, X., Hao, X., Yu, S., Li, D., Wang, S., Gan, W., Liu, Z., Yu, Y., Wang, Z., Wang, Y., Ning, W., Hou, Y., Wang, B., Wu, C., Xinzhi, W., Liu, Y., Wang, Y., Tang, D., Tu, D., Shang, L., Jiang, X., Tang, R., Lian, D., Liu, Q., & Chen, E. (2025). *ToolACE: Winning the Points of LLM Function Calling*. In ICLR 2025.  
-
 **BibTeX**  
 ```bibtex
 @inproceedings{2025-EMNLP-Scribe,