Add paper abstract to model card

This pull request improves the model card by adding the abstract of the associated paper, "[APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay](https://huggingface.co/papers/2504.03601)". This addition provides crucial context and a concise summary of the model's development and performance directly within the README, enhancing its utility for researchers and users.

README.md

@@ -1,10 +1,11 @@
 ---
-license: cc-by-nc-4.0
 datasets:
 - Salesforce/APIGen-MT-5k
 - Salesforce/xlam-function-calling-60k
 language:
 - en
+library_name: transformers
+license: cc-by-nc-4.0
 pipeline_tag: text-generation
 tags:
 - function-calling
@@ -14,7 +15,6 @@ tags:
 - qwen
 - pytorch
 - LLaMA-factory
-library_name: transformers
 ---
 
 <p align="center">
@@ -48,6 +48,9 @@ We've also refined the **chat template** and **vLLM integration**, making it eas
 <small><i>Comparative performance of larger xLAM-2-fc-r models (8B-70B, trained with APIGen-MT data) against state-of-the-art baselines on function-calling (BFCL v3, as of date 04/02/2025) and agentic (τ-bench) capabilities.</i></small>
 </p>
 
+## Paper Abstract
+
+Training effective AI agents for multi-turn interactions requires high-quality data that captures realistic human-agent dynamics, yet such data is scarce and expensive to collect manually. We introduce APIGen-MT, a two-phase framework that generates verifiable and diverse multi-turn agent data. In the first phase, our agentic pipeline produces detailed task blueprints with ground-truth actions, leveraging a committee of LLM reviewers and iterative feedback loops. These blueprints are then transformed into complete interaction trajectories through simulated human-agent interplay. We train a family of models -- the xLAM-2-fc-r series with sizes ranging from 1B to 70B parameters. Our models outperform frontier models such as GPT-4o and Claude 3.5 on τ-bench and BFCL benchmarks, with the smaller models surpassing their larger counterparts, particularly in multi-turn settings, while maintaining superior consistency across multiple trials. Comprehensive experiments demonstrate that our verified blueprint-to-details approach yields high-quality training data, enabling the development of more reliable, efficient, and capable agents. We open-source 5K synthetic data trajectories and the trained xLAM-2-fc-r models to advance research in AI agents.
 
 ## Table of Contents
 - [Model Series](#model-series)
@@ -175,7 +178,7 @@ output = llm.create_chat_completion(
                 "title": "Age",
                 "type": "integer"
               }
-            },
+            },\
             "required": [ "name", "age" ]
           }
         }
@@ -239,39 +242,38 @@ Additionally, please check our other amazing works regarding xLAM series and con
 
 ```bibtex
 @article{zhang2025actionstudio,
-  title={ActionStudio: A Lightweight Framework for Data and Training of Action Models},
-  author={Zhang, Jianguo and Hoang, Thai and Zhu, Ming and Liu, Zuxin and Wang, Shiyu and Awalgaonkar, Tulika and Prabhakar, Akshara and Chen, Haolin and Yao, Weiran and Liu, Zhiwei and others},
-  journal={arXiv preprint arXiv:2503.22673},
+  title={ActionStudio: A Lightweight Framework for Data and Training of Action Models},\
+  author={Zhang, Jianguo and Hoang, Thai and Zhu, Ming and Liu, Zuxin and Wang, Shiyu and Awalgaonkar, Tulika and Prabhakar, Akshara and Chen, Haolin and Yao, Weiran and Liu, Zhiwei and others},\
+  journal={arXiv preprint arXiv:2503.22673},\
   year={2025}
 }
 ```
 
 ```bibtex
 @article{zhang2024xlam,
-  title={xLAM: A Family of Large Action Models to Empower AI Agent Systems},
-  author={Zhang, Jianguo and Lan, Tian and Zhu, Ming and Liu, Zuxin and Hoang, Thai and Kokane, Shirley and Yao, Weiran and Tan, Juntao and Prabhakar, Akshara and Chen, Haolin and others},
-  journal={arXiv preprint arXiv:2409.03215},
+  title={xLAM: A Family of Large Action Models to Empower AI Agent Systems},\
+  author={Zhang, Jianguo and Lan, Tian and Zhu, Ming and Liu, Zuxin and Hoang, Thai and Kokane, Shirley and Yao, Weiran and Tan, Juntao and Prabhakar, Akshara and Chen, Haolin and others},\
+  journal={arXiv preprint arXiv:2409.03215},\
   year={2024}
 }
 ```
 
 ```bibtex
 @article{liu2024apigen,
-  title={Apigen: Automated pipeline for generating verifiable and diverse function-calling datasets},
-  author={Liu, Zuxin and Hoang, Thai and Zhang, Jianguo and Zhu, Ming and Lan, Tian and Tan, Juntao and Yao, Weiran and Liu, Zhiwei and Feng, Yihao and RN, Rithesh and others},
-  journal={Advances in Neural Information Processing Systems},
-  volume={37},
-  pages={54463--54482},
+  title={Apigen: Automated pipeline for generating verifiable and diverse function-calling datasets},\
+  author={Liu, Zuxin and Hoang, Thai and Zhang, Jianguo and Zhu, Ming and Lan, Tian and Tan, Juntao and Yao, Weiran and Liu, Zhiwei and Feng, Yihao and RN, Rithesh and others},\
+  journal={Advances in Neural Information Processing Systems},\
+  volume={37},\
+  pages={54463--54482},\
   year={2024}
 }
 ```
 
 ```bibtex
 @article{zhang2024agentohana,
-  title={AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning},
-  author={Zhang, Jianguo and Lan, Tian and Murthy, Rithesh and Liu, Zhiwei and Yao, Weiran and Tan, Juntao and Hoang, Thai and Yang, Liangwei and Feng, Yihao and Liu, Zuxin and others},
-  journal={arXiv preprint arXiv:2402.15506},
+  title={AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning},\
+  author={Zhang, Jianguo and Lan, Tian and Murthy, Rithesh and Liu, Zhiwei and Yao, Weiran and Tan, Juntao and Hoang, Thai and Yang, Liangwei and Feng, Yihao and Liu, Zuxin and others},\
+  journal={arXiv preprint arXiv:2402.15506},\
   year={2024}
 }
-```
-
+```