ToolLLM: Writing API Calls Superior to ChatGPT 4 – Superior to Gorilla LLM?

Introducing Tool LLM: Mastering Code Generation with Language Models

In recent times, we discussed Gorilla, a remarkable language model that surpassed GPT-4 by making faster API calls using over 1600 APIs. However, there is now an even more impressive tool called Tool LLM, which can facilitate code writing for over 16,000 APIs. This achievement is astonishing, considering how impressed we were with Gorilla's capabilities. In just a month, Tool LLM has raised the bar by 10 times, allowing developers to write APIs more efficiently.

Understanding Tool LLM and its Objective

For those unfamiliar with Tool LLM, it aims to create open-source, large-scale, and high-quality supervised fine-tuned data sets. These data sets are essential in enabling the development of powerful language models capable of mastering thousands of real-world APIs. Tool LLM accomplishes this by collecting high-quality instruction tuning data sets, which we will explore in detail in this article.

Exploring the Data Provided by Tool LLM

In this section, we will delve into the data that Tool LLM provides. We will also learn how to fine-tune and install it on our desktop for local use. Lastly, we will examine the research paper associated with Tool LLM to gain a deeper understanding of its purpose and capabilities.

A Note of Appreciation

Before we proceed, I would like to express my heartfelt gratitude to all of you. We recently reached a milestone of 16k subscribers, and I want to thank you for your continuous support and engagement with our content. Your support means the world to me, and I am committed to providing valuable and entertaining content in the future. I always strive to improve, as you can see from the recent changes in our editing styles and thumbnails. My goal is to provide you with the best possible information and make the most of your time. Thank you once again for your unwavering support.

Introduction to Tool LLM and its Revolutionary Approach

Tool LLM is a groundbreaking project that aims to enhance the capabilities of different language models. Its primary objective is to develop large-scale, top-tier instruction-tuned data sets. These data sets serve as crucial resources for creating robust and highly capable language models, equipped with a wide range of tool use capabilities. This concept is familiar if you have watched our video on Gorilla LM, where we explored how AI models generated API calls using various libraries such as Hugging Face and TensorFlow. However, Tool LLM takes it a step further by providing access to over 16,000 API calls. This open-source tool offers proficiency in generating code for diverse real-world use cases.

Parsing the Architecture of Tool LLM

To better understand Tool LLM, let's examine its five-step architecture. This architecture revolves around data collection, model enhancement, data creation, model fine-tuning, and practical demonstrations. The process begins with data collection, where high-quality instruction-tuned data sets are gathered, focusing on a diverse range of real-world APIs. Rapid API, a platform hosting numerous real-world APIs, is utilized for this purpose. Tool LLM has curated an impressive collection of 16,464 APIs, which serves as the basis for training the language models.

The model enhancement step involves leveraging the Chat GPT model, specifically the GPT 3.5 and Turbo 16k variants. These models are enhanced with advanced function call capabilities, empowering the Tool LLM model to effectively engage and respond to various API calls collected from Rapid API.

Data creation comes next, wherein the Chat GPT model and the collected APIs are used to automatically generate the instruction tuning data set. This data set is then utilized to fine-tune the Tool LLM model, refining its ability to comprehend and accurately generate code for a wide range of APIs.

Lastly, the practical demonstration showcases the proficiency of the Tool LLM model in responding to and generating API calls. This demonstration serves as proof of concept, highlighting the model's potential in real-world scenarios.

Installation and Utilization of Tool LLM

To make the most of Tool LLM, you need to install and fine-tune the model. Depending on your requirements, you can choose different methods such as fine-tuning with various models like Tool LLM, Chad GPT Laura, or utilizing the web UI.

The installation process involves cloning the Tool LLM repository, setting up Python as your code editor, and using Visual Studio Code to input API calls from different models, such as OpenAI.

If you opt for fine-tuning the Tool LLM models, you need to clone the repository and follow the provided instructions. On the other hand, if you focus on using specific API calls, you can integrate them by copying the code for the desired models, such as OpenAI, and including your API key.

To utilize the web UI, you can clone the web UI Tool LLM file, install the required dependencies, and then run it on your Local Host to write fast API calls using the fine-tuned Tool LLM model.

Unlocking the Power of Tool LLM in Interactive Scenarios

Tool LLM offers numerous applications, as demonstrated by its ability to comprehend and engage with various APIs. In the provided example, you can observe the model responding to a prompt requesting random love, success, and motivation quotes for a surprise party decoration. The model generates accurate and contextual code snippets by sourcing information from relevant APIs. This showcases the model's proficiency and its ability to utilize the vast list of 16,000 APIs efficiently.

Hitting Play: The Journey Continues

Before concluding, I want to express my heartfelt appreciation once again. We have accomplished a significant milestone, but this is just the beginning. I am committed to continuously improving and providing you with the best content possible. Your support and engagement mean the world to me, and I promise to keep working hard and ensuring the growth and success of this channel. Thank you for watching, and let's continue this journey together.

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Frequently Asked Questions

1. What is Gorilla and how does it compare to Tool LLM?

Answer: Gorilla is a language model that was able to write API calls faster than gpt4 with over 1600 APIs. Tool LLM, on the other hand, is a new language model that facilitates the writing of code for over 16,000 APIs, making it even more impressive than Gorilla.

2. What is the purpose of Tool LLM?

Answer: The purpose of Tool LLM is to construct open source large-scale and high-quality instruction tuning supervised fine-tuned data sets. It helps empower other language models to master thousands of real-world APIs by generating tool use capabilities.

3. How does Tool LLM collect its high-quality instruction tuning data sets?

Answer: Tool LLM collects its high-quality instruction tuning data sets by utilizing Rapid API, a platform that hosts a massive collection of real-world APIs. It gathers a diverse range of APIs, totaling 16,000, which is used for training the model to understand and generate accurate code for the APIs.

4. How can Tool LLM be installed and utilized?

Answer: There are several methods for installing and utilizing Tool LLM. One method is to clone the Tool Bench repository onto your desktop and follow the provided instructions. Another method is to utilize specific models, such as Chad GPT or Open AI, by inputting the API calls into the appropriate files. Additionally, Tool LLM can be hosted on a web UI by cloning the Web UI Tool Llama file and running it on your Local Host.

5. What are the benchmarks and limitations of Tool LLM?

Answer: The benchmarks of Tool LLM show that it can reach the performance of the Chad gbt turbo 16k model in terms of tool use, which is highly impressive. However, the limitations and specific details of Tool LLM can be found in the research paper associated with the model.