Tool LLM is an AI model that calls real-world APIs, trained on Toolbench's 16,000+ APIs. Researchers from Meta, Microsoft, Stanford and UC Berkeley claim it's 10 times more powerful than Gorilla AI, using decision-tree action plans to learn new APIs from documentation.
Questions this video answers
Over 16,000 APIs.
August 2023.
June 2023.
Tool LLM is "10 times more powerful" than Gorilla AI.
The AI world has just been shaken. Tool LLM is not just another AI model. It's a Titan dwarfing the renowned Gorilla AI model. Tool LLM is 10 times more powerful than Gorilla. This means it has 10 times more APIs and it has capabilities that other models don't have. In this video, I'll explain all the details about this insane model, how it stands against the gorilla, and why it's so important for the future of AI. But before we dive in the make sure you hit that like button and subscribe to my channel if you haven't already. Alright, first of all, what is Tool LLM? Well, it's an AI model that's skilled at working with real world restful APIs. This means it can connect with many online services and tools that talk to each other through APIs. For instance, it can ask Google Maps for directions, tell Spotify to play a song, or post something on Twitter, all by understanding simple language from users. So if you ask toollm to do a task, it it knows which API to use to get it done. How does it know all this? The secret is data. Tool LLM learned from a huge dataset called Toolbench, which has information on over 16,000 APIs. This dataset was made by experts from Meta, Microsoft, Stanford and UC Berkeley. They worked together on Tool LLM and shared their findings in a paper in August 2023. Tool LLM's goal is to help AI models get better at using many different real world APIs. It does this by using special data sets that pair simple language instructions with the right API calls from the toolbench dataset. Toolbench is more than just a set of APIs. It also includes directions and solutions for each API. The team used Chat GPT to write clear instructions for each API from its guidelines and examples, and they then had people show the step by step ways to use these APIs. These steps are shown as decision trees called DFSDT, which help tool LLM figure out the best way to use an API. So when you ask Tool LLM something in plain language, it looks in the toolbench data to find the right APIs. It then uses the DFSDT to pick the best API action for what you asked. After that, it does the action and gives you the result in a way you can understand or in the format you want. Impressive, isn't it? But that's not even the best part. Tool LLM can not only use the APIs it knows, but can also learn new ones. If you show it an API it hasn't seen before, it can learn to use it by looking at its guidelines and examples. This means Tool LLM stays current with new web tools and services that keep coming out okay, now how does Tool LLM stand against Gorilla? Let's break it down. Gorilla AI Model is a big language model made by UC Berkeley and Microsoft Research in June 2023. It's available for free on GitHub. This model can create API calls when given regular language instructions. It's based on data from three main machine learning torch hub, TensorFlow hub, and hugging Face Hub. These sources have a lot of APIs covering topics like computer vision, speech recognition, and more. When Gorilla AI Model was introduced, it was a big deal. It did better than GPT4 and Claude in making API calls. Plus it could quickly look up the most recent API documentation, which helped reduce mistakes and adjust to API updates. But Tool LLM had some clear advantages over Gorilla AI Model and I'll try to explain it in simple way so you get a better picture of how different these two models are. First off, when we talk about size and what these models have learned, Tool LLM is like the big sibling. Imagine Gorilla AI Model as a student who has read about 1,700 books. Pretty impressive, right? But then here comes Tool LM who has gone through a massive library of over 16,000 books. So naturally Tool LLM has a broader understanding and can tack more complex challenges. Now think about the variety of topics you chat about daily. While Gorilla AI Model is a genius in machine learning, it's kind of like it's stuck in its favorite subject. On the other hand, Tool LLM is that friend who knows a bit about everything, be it social media trends, the latest blockbuster movies, what's happening in schools, money saving tips or even health advice. And here's another cool thing. You know how things online keep changing. And updating Gorilla AI Model to stay updated relies on a special system to fetch the newest info about these changes. It's like always having to refer to the latest textbooks. But Tool LLM is a quick learner. Give it a new guide or some examples and it's ready to go, making it more nimble in adapting to the ever changing online world. So as you can see, Tool LLM is a clear winner over Gorilla AI Model in terms of size, scope and functionality. It is 10 times bigger than Gorilla and it can do things that no other model can. So this is definitely a future of AI. And I am sure that Tool LLM will also be a part of many fields education, healthcare, entertainment, finance and more. Plus it's open source, and you can help make Tool LLM even better by joining the community of people working on it. You can find Tool LLM on GitHub, which has its code, paper, data set and more. If you want to use this AI model, there are many ways. Like you can use it with Chat, GPT or Laura, a virtual assistant that listens and speaks using Tool LLM. There's also a web interface where you can enter or say your commands to Tool LLM. And if you are a developer or have technical skills, you can copy the repository and run the model on your computer. Make sure you have these programs Python 3.8, PyTorch 1.9, Transformers 4.9, Flask 2.0 2.26, NLTK 3.6, Beautiful Soup 4.9 and Git. To clone the repository, you can follow these steps. Open your terminal and navigate to the directory where you want to store the repo, type Git clone and paste the URL of the repository. From GitHub. Press enter to create your local clone. Now to run the model on your local machine, follow these steps. Navigate to the clone directory type Python Setup PY Install to install the dependencies and set up the package type python tool_llm py to launch the model, you will see a message saying Tool LLM is ready to use. Enter your natural language command or query below and if you want to learn more about the technical details and the evaluation results of Tool LLM, I highly recommend you to check out the research paper that the researchers published. It's a very well written and comprehensive paper that explains the architecture, the data creation process and the model enhancement step and the performance metrics of Tool LLM. Now, this model is already insanely powerful, but there's still room for growth. We need to delve deeper into the tool bench data set. Right now it doesn't encompass every API out there, so enriching it with diverse APIs from various sectors will make it even more versatile. Furthermore, accuracy is paramount. Sometimes Tool LLM's answers aren't spot on, which means refining its feedback mechanisms could prove invaluable. Another intriguing aspect Peeling back the Layers to truly understand how Tool LLM thinks, the model's decision making process remains somewhat of a mystery, and shedding light on its inner workings will help build trust. Lastly, we can't ignore the bigger picture. With its potential to revolutionize numerous industries. We must weigh the pros and cons of this model, ensuring its use aligns with our values and ethics for a responsible future I hope you enjoyed this video and if you did, please give it a thumbs up and subscribe to AI Revolution. And if you have any questions or comments about Tool LLM or Gorilla, please leave them down below. I'd love to hear from you. And now I have a question for you. How do you see the impact of Tool LLM evolving in the coming years? Let me know your thoughts and predictions in the comments section below. Thank you so much for watching. I'll see you in the next video.
