LLM Tool Use - GPT4o-mini, Groq & Llama.cpp
TLDRThe video explores integrating large language models (LLMs) with tools and APIs for real-time data access. It demonstrates using GPT-40 Mini for robust integration, Groq API for speed, and open-source models for local tool use. The host covers setting up function definitions, metadata, and error handling for robust systems. Examples include weather information retrieval and outfit suggestions based on the weather. The video also touches on zero-shot function calling with quantized models and running them locally using Llama.cpp, highlighting the advancements in LLM tool use and its practical applications.
Takeaways
- ๐ Tool use or function calling is a technique to integrate large language models (LLMs) with external data sources like APIs, enhancing their capabilities to provide real-time information.
- ๐ค The video demonstrates integrating LLMs using GPT-40 Mini, Groq API, and open-source models like Phi-3 Mini and Llama CPP, focusing on different aspects like cost, latency, and robustness.
- ๐ Setting up tool use involves defining function metadata, creating a prompt with this metadata, and ensuring the language model understands which functions are available for calling.
- ๐ Functions should be clearly defined with types, descriptions, and examples to help the language model understand the inputs, outputs, and usage context, enhancing the accuracy of function calls.
- ๐ ๏ธ Metadata is crucial as it provides a structured way for the language model to know what functions it can access, and it's recommended to generate this programmatically from the functions for consistency.
- ๐ The process involves a recursive loop where the language model makes a function call, the function is executed, and the result is fed back into the model, potentially leading to multiple calls for complex queries.
- ๐ OpenAI's GPT-40 Mini is highlighted as a cost-effective model with good performance for tool use, suitable for applications needing reliable results at a lower cost.
- ๐ Groq API is recommended for the fastest and lowest latency integration, ideal for real-time applications where speed is critical.
- ๐ก Zero-shot function calling with models like Phi-3 Mini shows promising results, demonstrating that smaller models can effectively perform function calls without specific fine-tuning.
- ๐ง Running models like Llama CPP locally on devices like Macs (with M1/M2 chips) is possible, offering a way to perform inference on personal devices, though performance may vary based on the model's size and quantization.
Q & A
What is the main topic of the video?
-The main topic of the video is the integration of large language models (LLMs) with tool use or function calling, demonstrating various approaches and models for achieving this.
What is the purpose of tool use or function calling in LLMs?
-The purpose of tool use or function calling in LLMs is to enable the models to access real-time data or external information, such as market data or customer database information, by calling external functions or APIs.
Which models does the video discuss for tool use?
-The video discusses using GPT-40 Mini, Phi-3 Mini, and models from Groq for tool use, as well as running a quantized Phi-3 Mini model locally using Llama CPP.
What is the significance of metadata in tool use?
-Metadata is significant in tool use as it provides a structured way to inform the language model about the functions or tools it has access to, which is essential for the model to make appropriate function calls.
How does the video demonstrate the process flow of tool use?
-The video demonstrates the process flow of tool use through a diagram, explaining how an input question leads to a function call, the retrieval of information, and the feeding of this information back into the language model to generate a response.
What are some best practices for setting up function definitions in tool use?
-Best practices for setting up function definitions include defining the types of inputs and outputs, providing a detailed description, returning a dictionary for consistency, validating inputs, and programmatically generating metadata from the functions.
How does the video handle the integration of tool use with open-source language models?
-The video shows how to integrate tool use with open-source language models by running a quantized Phi-3 Mini model on a local laptop using Llama CPP, demonstrating that tool use can be implemented even on personal devices.
What is the role of error management and validation in function definitions?
-Error management and validation are crucial in function definitions as they help ensure that the language model receives correct and useful information. They allow the model to handle errors properly and potentially self-correct in subsequent iterations.
How does the video compare the performance of different models in tool use?
-The video compares the performance of different models in tool use by demonstrating their ability to handle function calls, manage metadata, and generate accurate responses. It highlights the strengths and limitations of each model in various scenarios.
What are the final tips provided in the video for effective tool use?
-The final tips provided in the video for effective tool use include ensuring the language model is well-prepared to handle function calls, setting up functions with clear inputs and outputs, and using scripts to manage metadata generation and function execution.
Outlines
๐ค Introduction to Tool Use with Large Language Models
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.
๐ Understanding Metadata and Function Definitions for Tool Use
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.
๐ง Practical Steps for Tool Use Implementation
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.
๐ฅ๏ธ Demonstrating Tool Use with OpenAI, Grok, and Local Models
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.
๐ Recursion and Function Execution in Tool Use Scripts
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.
๐ Exploring Zero-Shot Function Calling with Open-Source Models
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.
๐ง Fine-Tuning and Zero-Shot Function Calling Comparison
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.
๐ก Final Thoughts on Tool Use and Model Recommendations
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.
Mindmap
Keywords
๐กLLM (Large Language Model)
๐กTool Use
๐กFunction Calling
๐กAPI (Application Programming Interface)
๐กMetadata
๐กGPT-40 Mini
๐กZero-Shot Learning
๐กQuantization
๐กGrock API
๐กLlama.cpp
Highlights
Integrating Large Language Models (LLMs) with the internet or APIs can be achieved through tool use or function calling.
GPT-40 Mini is highlighted as a cost-effective model with tool use capabilities, outperforming GPT-3.5.
The video demonstrates the setup process for tool use with a focus on function definitions and metadata.
Metadata is crucial for robust systems that can report and handle errors effectively.
Grok API is presented as a fast and low-latency option for tool use integration.
Examples of tool use with Phi-3 Mini model showcase its surprising performance with zero-shot function calling.
The video provides a detailed process flow diagram explaining tool use and function calling.
Function calling involves structured requests, typically in JSON format, for the language model to access external data.
The importance of preparing clean functions with clear inputs, outputs, and examples is emphasized for better language model performance.
A script is introduced to programmatically generate metadata from function definitions for consistency.
The video includes a practical demonstration of querying the LLM using GPT-4O Mini for weather information.
Grok API's model for tool use is tested, comparing zero-shot and fine-tuned performances.
The video covers final tips and background information on creating robust systems for tool use in LLMs.
An example of running a quantized 5.3 billion parameter model locally on a Mac using Llama.cpp is provided.
The video concludes with a discussion on the advancements in LLMs and their ability to perform zero-shot function calling effectively.
A repository is mentioned for accessing the scripts used in the video for advanced inference techniques.
The video highlights the flexibility and potential of using smaller models like Phi 3 mini for on-device function calling.
A comparison is made between different models and approaches for tool use, emphasizing the strengths of each.