xlam-function-calling-60k

APIGen Function-Calling Datasets Paper | Website | Models This repo contains 60,000 data collected by APIGen, an automated data generation pipeline designed to produce verifiable high-quality datasets for function-calling applications. Each data in our dataset is verified through three hierarchical stages: format checking, actual function executions, and semantic verification, ensuring its reliability and correctness. We conducted human evaluation over 600 sampled data points, and the correct rate is above 95%, where the remaining 5% have minor issues like inaccurate arguments, etc. The overall framework for the dataset collection procedure is shown below. See more details at our project homepage. 🎉 News [July 2024]: We are thrilled to announce the release of our two function-calling models: xLAM-1b-fc-r and xLAM-7b-fc-r. These models have achieved impressive rankings, placing #3 and #25 on the Berkeley Function-Calling Leaderboard, outperforming many significantly larger models. We also provide their GGUF files, which can be readily deployed on personal devices. Stay tuned for more powerful models coming soon. [July 2024]: We've addressed issues mentioned in discussion #8 by regenerating 1,896 affected data points. Thank you to the community for identifying these issues and helping us further improve the quality of our dataset! [June 2024]: We are pleased to see our work featured by VentureBeat and 新智元. What is a Function-Calling Agent? Function-calling agents are capable of executing functional API calls from plain language instructions. Imagine asking for today’s weather in Palo Alto. In response, a function-calling agent swiftly interprets this request, taps into the appropriate API—for example, get_weather("Palo Alto", "today")—and fetches real-time weather data. This advanced capability significantly broadens the practical applications of LLMs, allowing them to seamlessly interact with various digital platforms, from social media to financial services, enhanci