Update model card with ActionStudio paper link, library_name, and updated citation

README.md

@@ -1,25 +1,24 @@
 ---
-extra_gated_heading: >-
-  Acknowledge to follow corresponding license to access the
-  repository
-extra_gated_button_content: Agree and access repository
-extra_gated_fields:
-  First Name: text
-  Last Name: text
-  Country: country
-  Affiliation: text
-license: cc-by-nc-4.0
 datasets:
 - Salesforce/xlam-function-calling-60k
 language:
 - en
+license: cc-by-nc-4.0
 pipeline_tag: text-generation
+library_name: transformers
 tags:
 - function-calling
 - LLM Agent
 - tool-use
 - deepseek
 - pytorch
+extra_gated_heading: Acknowledge to follow corresponding license to access the repository
+extra_gated_button_content: Agree and access repository
+extra_gated_fields:
+  First Name: text
+  Last Name: text
+  Country: country
+  Affiliation: text
 ---
 
 <p align="center">
@@ -27,7 +26,8 @@ tags:
 </p>
 <p align="center">
   <a href="https://apigen-pipeline.github.io/">[Homepage]</a>  |  
-  <a href="https://arxiv.org/abs/2406.18518">[Paper]</a> | 
+  <a href="https://arxiv.org/abs/2406.18518">[APIGen Paper]</a> | 
+  <a href="https://huggingface.co/papers/2503.22673">[ActionStudio Paper]</a> |
   <a href="https://discord.gg/tysWwgZyQ2">[Discord]</a> | 
   <a href="https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k">[Dataset]</a> | 
   <a href="https://github.com/SalesforceAIResearch/xLAM">[Github]</a>
@@ -59,8 +59,6 @@ We provide a series of xLAMs in different sizes to cater to various applications
 | xLAM-7b-fc-r           | 6.91B          | 4k            | July 17, 2024| Function-calling| [🤗 Link](https://huggingface.co/Salesforce/xLAM-7b-fc-r) | [🤗 Link](https://huggingface.co/Salesforce/xLAM-7b-fc-r-gguf) |
 | xLAM-v0.1-r           | 46.7B          | 32k            | Mar. 18, 2024 |General,  Function-calling | [🤗 Link](https://huggingface.co/Salesforce/xLAM-v0.1-r) | -- |
 
-
-
 The `fc` series of models are optimized for function-calling capability, providing fast, accurate, and structured responses based on input queries and available APIs. These models are fine-tuned based on the [deepseek-coder](https://huggingface.co/collections/deepseek-ai/deepseek-coder-65f295d7d8a0a29fe39b4ec4) models and are designed to be small enough for deployment on personal devices like phones or computers.
 
 We also provide their quantized [GGUF](https://huggingface.co/docs/hub/en/gguf) files for efficient deployment and execution. GGUF is a file format designed to efficiently store and load large language models, making GGUF ideal for running AI models on local devices with limited resources, enabling offline functionality and enhanced privacy.
@@ -205,10 +203,26 @@ def convert_to_xlam_tool(tools):
 
 # Helper function to build the input prompt for our model
 def build_prompt(task_instruction: str, format_instruction: str, tools: list, query: str):
-    prompt = f"[BEGIN OF TASK INSTRUCTION]\n{task_instruction}\n[END OF TASK INSTRUCTION]\n\n"
-    prompt += f"[BEGIN OF AVAILABLE TOOLS]\n{json.dumps(xlam_format_tools)}\n[END OF AVAILABLE TOOLS]\n\n"
-    prompt += f"[BEGIN OF FORMAT INSTRUCTION]\n{format_instruction}\n[END OF FORMAT INSTRUCTION]\n\n"
-    prompt += f"[BEGIN OF QUERY]\n{query}\n[END OF QUERY]\n\n"
+    prompt = f"[BEGIN OF TASK INSTRUCTION]
+{task_instruction}
+[END OF TASK INSTRUCTION]
+
+"
+    prompt += f"[BEGIN OF AVAILABLE TOOLS]
+{json.dumps(xlam_format_tools)}
+[END OF AVAILABLE TOOLS]
+
+"
+    prompt += f"[BEGIN OF FORMAT INSTRUCTION]
+{format_instruction}
+[END OF FORMAT INSTRUCTION]
+
+"
+    prompt += f"[BEGIN OF QUERY]
+{query}
+[END OF QUERY]
+
+"
     return prompt
     
 # Build the input and start the inference
@@ -304,11 +318,15 @@ This release is for research purposes only in support of an academic paper. Our
 ## Citation
 
 If you find this repo helpful, please cite our paper:
+
 ```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 Kokane, Shirley and Tan, Juntao and Yao, Weiran and Liu, Zhiwei and Feng, Yihao and others},
-  journal={arXiv preprint arXiv:2406.18518},
-  year={2024}
+@misc{tan2025actionstudio,
+      title={ActionStudio: A Lightweight Framework for Data and Training of Action Models}, 
+      author={Zhenxiong Tan and Jianguo Zhang and Tian Lan and Thai Hoang and Linh Tran and Ming Zhu and Shervin Raissi and Caiming Xiong},
+      year={2025},\
+      eprint={2503.22673},\
+      archivePrefix={arXiv},\
+      primaryClass={cs.LG},\
+      url={https://arxiv.org/abs/2503.22673}\
 }
-```
+```