Salesforce
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xLAM-1b-fc-r

@@ -5,7 +5,12 @@ license: cc-by-nc-4.0
 <p align="center">
 <img width="300px" alt="xLAM" src="https://huggingface.co/Salesforce/xLAM-v0.1-r/resolve/main/xlam-no-background.png">
 </p>
-<p align="center"><a href="https://apigen-pipeline.github.io/">[🏠Homepage]</a>  |  <a href="https://coder.deepseek.com/">[📄 Paper]</a> | <a href="https://coder.deepseek.com/">[📚 Dataset]</a></p>
 <hr>
 Welcome to the xLAM model family! [Large Action Models (LAMs)](https://blog.salesforceairesearch.com/large-action-models/) are advanced large language models designed to enhance decision-making and translate user intentions into executable actions that interact with the world. LAMs autonomously plan and execute tasks to achieve specific goals, serving as the brains of AI agents. They have the potential to automate workflow processes across various domains, making them invaluable for a wide range of applications.
@@ -47,7 +52,7 @@ alt="drawing" width="620"/>
 Function-calling, or tool use, is one of the key capabilities for AI agents. It requires the model not only understand and generate human-like text but also to execute functional API calls based on natural language instructions. This extends the utility of LLMs beyond simple conversation tasks to dynamic interactions with a variety of digital services and applications, such as retrieving weather information, managing social media platforms, and handling financial services.
 The instructions will guide you through the setup, usage, and integration of `xLAM-1b-fc-r` with HuggingFace and vLLM.
-We will first introduce the basic usage, and then walk through the provided tutorial and example scripts.
 ### Framework Versions
@@ -209,7 +214,7 @@ We provide example scripts to deploy our model with `vllm` and run inferences. F
 pip install vllm openai argparse jinja2
 ```
-The example scripts are located in the `examples` folder.
 #### 1. Test Prompt Template

 <p align="center">
 <img width="300px" alt="xLAM" src="https://huggingface.co/Salesforce/xLAM-v0.1-r/resolve/main/xlam-no-background.png">
 </p>
+<p align="center">
+  <a href="https://apigen-pipeline.github.io/">[Homepage]</a>  |
+  <a href="https://coder.deepseek.com/">[Paper]</a> |
+  <a href="https://coder.deepseek.com/">[Dataset]</a> |
+  <a href="https://github.com/SalesforceAIResearch/xLAM">[Github]</a>
+</p>
 <hr>
 Welcome to the xLAM model family! [Large Action Models (LAMs)](https://blog.salesforceairesearch.com/large-action-models/) are advanced large language models designed to enhance decision-making and translate user intentions into executable actions that interact with the world. LAMs autonomously plan and execute tasks to achieve specific goals, serving as the brains of AI agents. They have the potential to automate workflow processes across various domains, making them invaluable for a wide range of applications.
 Function-calling, or tool use, is one of the key capabilities for AI agents. It requires the model not only understand and generate human-like text but also to execute functional API calls based on natural language instructions. This extends the utility of LLMs beyond simple conversation tasks to dynamic interactions with a variety of digital services and applications, such as retrieving weather information, managing social media platforms, and handling financial services.
 The instructions will guide you through the setup, usage, and integration of `xLAM-1b-fc-r` with HuggingFace and vLLM.
+We will first introduce the basic usage, and then walk through the provided tutorial and example scripts in the [examples](https://huggingface.co/Salesforce/xLAM-1b-fc-r/tree/main/examples) folder.
 ### Framework Versions
 pip install vllm openai argparse jinja2
 ```
+The example scripts are located in the [examples](https://huggingface.co/Salesforce/xLAM-1b-fc-r/tree/main/examples) folder.
 #### 1. Test Prompt Template