LLM Tool Use - GPT4o-mini, Groq & Llama.cpp
TLDRThis video explores integrating Large Language Models (LLMs) with external tools for enhanced functionality. It discusses various methods, from using GPT-40 Mini for robust tool integration to leveraging Groq's low-latency API. The presenter also covers open-source models, showcasing zero-shot function calling with a quantized 3.5 billion parameter model running on a Mac. The tutorial includes setting up function definitions, metadata, and error handling for reliable results, with examples using different models and APIs, ultimately guiding viewers on implementing tool use in their AI applications.
Takeaways
- 😀 The video discusses integrating Large Language Models (LLMs) with tool use or function calling for accessing real-time data or APIs.
- 🔍 The presenter introduces GPT-40 Mini as a cost-effective model with tool use capabilities, comparing it to GPT-3.5.
- 🌐 The video covers different approaches to tool use, including using Groq API for low latency and open-source models like Phi-3 Mini for zero-shot function calling.
- 📚 The importance of setting up function definitions and metadata is emphasized for building robust systems that can handle errors effectively.
- 🔧 The process flow diagram illustrates how tool use works, from input question to accessing external data and feeding it back into the language model.
- 💻 The video demonstrates how to query LLMs using different models like GPT-40 Mini, Phi-3 Mini, and Groq API, highlighting their performance in function calling.
- 📈 The presenter shows an example of zero-shot function calling with a quantized 3.5 billion parameter model running on a Mac, showcasing its capabilities.
- 🛠️ Tips are provided for defining functions, including specifying types, providing descriptions, returning dictionaries, and validating inputs to improve language model outputs.
- 🔗 The video explains how to programmatically generate metadata from function definitions, which is crucial for the language model to understand available tools.
- 🏠 The final section covers running a quantized model locally using Llama CPP, providing instructions for setting up and using the model on a personal computer.
Q & A
What is the main topic of the video 'LLM Tool Use - GPT4o-mini, Groq & Llama.cpp'?
-The main topic of the video is the integration of Large Language Models (LLMs) with tool use or function calling, demonstrating different approaches to connect an LLM to the internet or APIs for real-time data access.
What are the different sections covered in the video?
-The video covers setting up tool use, function definitions, metadata and tools list, querying the LLM with examples of GPT-40 Mini, Phi-3 Mini, Groq API, and Groq with Roder, as well as final tips and background information on the video.
Why is metadata important when integrating tool use with LLMs?
-Metadata is important because it provides a structured way to inform the LLM about the functions or tools it has access to, which is essential for building robust systems that can report proper errors and handle them effectively.
What is the purpose of the function definitions in tool use with LLMs?
-Function definitions serve to clearly communicate to the LLM the inputs and outputs expected by each function, along with examples and validation rules, ensuring that the LLM can correctly call these functions when needed.
How does the video demonstrate the use of GPT-40 Mini for tool use?
-The video shows how to set up and use GPT-40 Mini, a cost-effective model with tool use capabilities, by creating prompts that include metadata and function calls, and then executing those function calls to retrieve real-time data.
What is the significance of zero-shot function calling in the context of the video?
-Zero-shot function calling is significant as it allows the LLM to perform tool use without prior fine-tuning, demonstrating the model's ability to understand and execute function calls based on the provided metadata and prompts alone.
How is the Groq API used in the video to demonstrate tool use?
-The Groq API is used to show the fastest and lowest latency way of integrating tool calling, by running a model that supports tool use and executing function calls to retrieve real-time data for the LLM to process.
What is the role of the 'get weather' function in the video's examples?
-The 'get weather' function is used as an example to illustrate how an LLM can access real-time data by making an API call to a weather service, demonstrating the practical application of tool use in obtaining up-to-date information.
How does the video address the issue of infinite loops in tool use?
-The video suggests setting a maximum recursion depth to prevent infinite loops, ensuring that the LLM will stop making function calls after a certain number of iterations if it does not provide a satisfactory answer.
What are the final tips provided in the video regarding tool use with LLMs?
-The final tips include ensuring the language model is well-prepared to handle function calls, setting up functions with clear inputs and outputs, validating inputs within the functions, and programmatically generating metadata from function definitions to avoid inconsistencies.
Outlines
🤖 Introduction to LLM Integration Techniques
The video introduces various methods for integrating Large Language Models (LLMs) with external data sources, emphasizing tool use or function calling as a robust approach. The presenter plans to demonstrate using GPT-40 Mini, the Grox API, and open-source language models for real-time data integration. The session is structured to cater to both beginners, who will learn the steps for reliable results, and advanced users, who will see an example of tool calling with a quantized model running on the presenter's Mac.
🔍 Deep Dive into Tool Use and Function Calling
This paragraph delves into the specifics of setting up tool use, including function definitions and metadata construction, which is vital for building robust systems capable of error reporting and handling. The presenter outlines the process flow of tool use, from inputting a question to accessing real-time data via function calls, and emphasizes the importance of structured metadata for the language model to understand available functions.
📚 Essential Steps for Function Definition and Metadata Creation
The video script provides a checklist for defining functions clearly, including type definitions, descriptions, and validation to ensure the language model interacts with functions correctly. It also recommends programmatically generating metadata from function definitions for consistency and reliability, and discusses the importance of error management and validation within functions.
🔧 Practical Examples of Function Calling with LLMs
The presenter discusses practical examples of using function calling with different models, such as GPT-40 Mini, Phi-3 Mini, and the Grock API. The script covers how to set up function calls, the importance of metadata, and executing function calls based on the language model's response. It also highlights the process of feeding real-time data back into the model to refine answers.
🔄 Recursion and Looping in Tool Use
This section explains the recursive nature of tool use, where the language model may need to make multiple calls to functions to gather information. The script outlines how to structure prompts for the language model, execute function calls, and handle the responses, including the use of a max recursion depth to prevent infinite loops.
🛠️ Advanced Function Calling Techniques and Tips
The video script touches on advanced techniques like parallel function calling and emphasizes the need for a well-performing language model to handle function calls effectively. It provides tips for preparing functions, converting them to metadata, and executing them within a recursive loop, ensuring the model can self-correct and provide accurate answers.
🌐 Local Deployment of LLMs for Function Calling
The final part of the script discusses running a quantized model locally on a laptop using Lama CPP for fast inference. It provides a brief guide on setting up the model with the necessary environment variables and API endpoints, and demonstrates how to execute function calls locally, highlighting the potential for on-device AI applications.
Mindmap
Keywords
💡LLM (Large Language Model)
💡Tool Use
💡Function Calling
💡Metadata
💡GPT-40 Mini
💡Groq API
💡Zero-Shot Function Calling
💡Llama.cpp
💡Quantization
💡Roder
Highlights
Demonstration of integrating a large language model (LLM) using GPT-40 Mini for tool use.
Introduction of Groq API for the fastest and lowest latency integration of tool calling.
Exploration of integrating tool calling within open-source language models for local use.
Explanation of tool use for accessing real-time market data or customer database information.
Discussion on setting up function definitions and building metadata for robust system error handling.
Illustration of querying the LLM with examples using GPT-40 Mini, Phi-3, and Groq API.
Showcasing zero-shot function calling with a quantized 3.5 billion parameter model on a Mac.
Description of the process flow diagram for tool use and function calling.
Emphasis on the importance of preparing functions with clear inputs, outputs, and examples.
Recommendation to programmatically generate metadata from function definitions for reliability.
Highlighting the need for error management and validation within functions for self-correction.
Introduction of the Trellis Research Advanced Inference repository for accessing scripts.
Explanation of how to convert functions into metadata programmatically using function utils.
Demonstration of recursive function calling in a script to handle multiple tool calls.
Use of GPT-40 Mini for answering questions without function calls and handling tool calls for weather information.
Zero-shot function calling with Phi-3 Mini model showing surprisingly good performance.
Comparison of zero-shot and fine-tuned models using Groq API for tool use.
Discussion on running a quantized Phi-3 Mini model locally on a laptop using Llama CPP.
Final tips on using different models for tool use, emphasizing the flexibility of zero-shot approaches.