LLM Tool Use - GPT4o-mini, Groq & Llama.cpp

The speaker introduces the concept of integrating large language models (LLMs) with external data sources through tool use or function calling. The video aims to demonstrate various methods for this integration, starting with the use of GPT-40 Mini, followed by the fastest method using the Grox API, and finally, the most robust open-source alternatives. The session is structured to cater to both beginners, who will learn the steps for setting up tool use, and advanced users, who will see a zero-shot example using a quantized 3.5 billion parameter model running on the presenter's Mac. The video promises a practical walkthrough, including a process flow diagram and detailed discussions on function definitions, metadata, and error handling.

The paragraph delves into the technical aspects of setting up tool use, emphasizing the importance of metadata and function definitions. Metadata is described as a structured, flattened form of functions, which should be programmatically generated from the functions themselves for reliability. The speaker suggests focusing on writing clean functions and relying on scripts to generate metadata systematically. The paragraph also covers the process of building prompts for the language model, which includes injecting metadata followed by the user's query. The language model's response should be structured, typically as a JSON object, indicating the need for more information from the defined functions.

This section provides practical guidance on implementing tool use, starting with a checklist for defining functions, which includes specifying types, descriptions, and validation. The speaker also recommends returning dictionaries for consistency and generating metadata programmatically to avoid inconsistencies. The paragraph discusses the importance of error management and validation within functions, suggesting that clear error messages can help the language model self-correct in subsequent iterations. Additionally, the speaker advises on avoiding infinite loops by imposing time limits on function calls.

The speaker outlines the process of using tool use with different models, including OpenAI, Grok, and running Phi-3 Mini locally. The paragraph explains how to prepare functions and metadata for querying these models and highlights the importance of using a consistent structure for function calling across different APIs. It also touches on the syntax differences for function calling models on Grok and provides a brief overview of creating function metadata from the original functions defined in the code.

The paragraph explains the recursive nature of tool use scripts, which involve making an initial call to the language model, executing any function calls made by the model, and then making a second call to the model with the retrieved information. The process continues until the model provides a response without a function call or until a maximum recursion depth is reached. The script includes error handling to manage situations where the model's response is not a valid function call.

The speaker discusses the capabilities of zero-shot function calling with open-source models like Phi-3 Mini, which despite its smaller size, performs surprisingly well. The paragraph covers the process of setting up an open AI-style endpoint for running the model and emphasizes the flexibility and potential of zero-shot function calling compared to fine-tuning models. It also provides instructions for accessing and using one-click templates for various models available in a public repository.

This section compares the performance of fine-tuned models with zero-shot function calling on the same tasks. The speaker notes that while fine-tuned models can be very effective, zero-shot calling offers more flexibility and often better results, especially for applications with many edge cases. The paragraph provides insights into the strengths and limitations of both approaches and suggests that zero-shot calling is currently the preferred method for its adaptability.

The speaker concludes the video by summarizing the key points discussed and providing recommendations for using tool use with different models. They highlight the reliability and cost-effectiveness of GPT-40 Mini, the high-speed performance of Grok with zero-shot function calling, and the potential of running quantized models locally on devices like Macs with M1 or M2 chips. The paragraph also mentions the possibility of using other models like Mestral Nemo for zero-shot function calling and invites viewers to share their experiences and questions in the comments.