The video introduces open-source function calling models by Grok, including 8 billion and 70 billion models available for public use on platforms like Grok and Hugging Face. These models, trained on synthetic data with the help of Glaive AI, excel in function calling tasks, outperforming proprietary models and offering flexibility for users to define and integrate custom tools within the framework for enhanced natural language processing applications.
In the video, Grok announced the release of two open-source models, an 8 billion and a 70 billion model, specifically trained for function calling. These models are available on the Grok platform and also on the Hugging Face platform for public use. They have been benchmarked using the Berkeley function calling leaderboards, where the 70 billion model ranked first and the 8 billion model ranked third, outperforming many proprietary models in function calling tasks. The models were trained on synthetic data, created with the help of a startup called Glaive AI, known for generating high-quality datasets for function calling tasks.
The video provides a demonstration of using the Grok model for function calling tasks, showcasing how the model can process requests for mathematical expressions and internet searches. The tool definitions for different tasks are set up using a standard schema, allowing the model to identify and execute the appropriate function based on the user query. However, there are limitations observed in the model’s ability to handle queries that do not require a specific tool, leading to a need for improvements in handling general queries without tool dependencies.
Custom tools for tasks like Duck Duck Go searches and Wikipedia searches are integrated into the model, demonstrating how users can define and utilize their own tools within the function calling framework. The video highlights the importance of defining tools accurately to ensure the model generates relevant responses. Query rewriting techniques are suggested to enhance the model’s performance in understanding user intent and selecting the appropriate tool for the given query, improving overall user experience.
The video emphasizes the value of experimentation with the model to assess its effectiveness for specific use cases. While the open-source function calling model from Grok shows promise in outperforming proprietary models in benchmarking tests, individual users may need to fine-tune the tool definitions and query processing to optimize performance for their particular applications. The availability of these models on platforms like Grok and Hugging Face enables developers to explore and leverage the capabilities of state-of-the-art function calling models for various tasks, potentially enhancing the efficiency and accuracy of natural language processing applications.
Overall, the video provides insights into the development and application of open-source function calling models, showcasing their performance in benchmarking tests and practical demonstrations. By offering access to these models on popular platforms, Grok aims to empower developers and researchers to harness the capabilities of advanced natural language processing models for function calling tasks. The video encourages users to experiment with the models, refine tool definitions, and explore different applications to leverage the full potential of these open-source resources for enhancing language processing capabilities.