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 regenerat…
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