Uncovering the Truth About Large Language Models

Table of Contents

1. Introduction
2. Getting Access to Open Source Large Language Models
3. Building the App Using Streamlit
4. Comparing OpenAI with Open Source Models
   • Basic Chat Q&A
   • Difference between Nuclear Fusion and Fission
   • Writing an Email about a Sale
   • Writing a Poem about Sunflowers
   • Dropping a Block on Formula One
   • Testing Few Shot Prompting
   • Calculating Fibonacci Sequences
5. Refactoring the App to Use Python Tool Chain with OpenAI
6. Conclusion
7. FAQ

Article

Introduction

In this article, we will explore the capabilities of a large language model app that can answer questions, complete tasks, summarize text, and even write and run Python code. What's more, this app is completely open source and free to use. We will discuss how to get access to open source large language models, build the app using Streamlit, compare the app's performance with OpenAI, and even refactor it to use Python tool chain with OpenAI.

Getting Access to Open Source Large Language Models

One of the easiest ways to get access to open source large language models is through GPT-4-All. This platform allows You to use a chat-like interface to Interact with the models locally on your machine. By downloading the model weights to your machine, you can leverage them in your own applications. The GPT-4-All GUI provides a user-friendly installation process. More detailed steps on running the code can be found on their Website.

Building the App Using Streamlit

To build the app using Streamlit, we first need to import the necessary dependencies. We will use the Streamlit framework as our app development framework. Once we have imported the dependencies, we can Create the app by setting the title and creating a prompt text box. We can then write a simple trigger that activates when the enter key is pressed. This trigger will display the prompt on the screen.

Comparing OpenAI with Open Source Models

In order to compare the performance of the large language model app with OpenAI, we conducted several tests. The tests included asking basic chat questions, inquiring about the difference between nuclear fusion and fission, writing an email about a sale, requesting a poem about sunflowers, dropping a block on Formula One, testing few-shot prompting, and calculating Fibonacci sequences. We compared the responses of the app with those of OpenAI's Text-DaVinci-003 and Mosaic ML's 7 billion parameter model, as well as Nomic AI's 13 billion parameter snoozy model.

Basic Chat Q&A

When asking simple questions in a chat-like format, the app performed reasonably well, providing coherent responses. OpenAI's Text-DaVinci-003 and Mosaic ML's model also provided satisfactory answers. However, it should be noted that setting up Mosaic ML's model was challenging.

Difference between Nuclear Fusion and Fission

When asking about the difference between nuclear fusion and fission, the app, Text-DaVinci-003, and snoozy provided accurate responses. However, Mosaic ML's model seemed to struggle with this particular question.

Writing an Email about a Sale

When tasked with writing an email to inform customers about a sale, both the app and Text-DaVinci-003 performed well, mentioning a discount. However, Mosaic ML's model generated a strange response with brackets and a dollar sign at the start.

Writing a Poem about Sunflowers

When asked to write a poem about sunflowers, Text-DaVinci-003 produced a beautiful literary composition, while snoozy's response was also poetic, with rhyming throughout. Mosaic ML's model, on the other HAND, seemed to mistake "sunflowers" for "sunnies."

Dropping a Block on Formula One

When requesting a summary of information about Formula One, both Text-DaVinci-003 and snoozy provided coherent summaries. However, Mosaic ML's model struggled to generate a proper response.

Testing Few Shot Prompting

When testing the app's ability to reason with few-shot prompting, Text-DaVinci-003 correctly responded to a sequence of numbers with an evaluation of whether the sum of odd numbers in the sequence is even or odd. Snoozy, however, failed to produce the desired response, while vicuna, another llama model derivative, generated a response that missed the mark.

Calculating Fibonacci Sequences

In a challenge to calculate the 12th number in a Fibonacci sequence, Text-DaVinci-003 correctly identified it as 144, while snoozy correctly identified it as 89. However, snoozy took significantly longer to generate the response on a CPU compared to Text-DaVinci-003.

Refactoring the App to Use Python Tool Chain with OpenAI

In addition to comparing the app's performance with OpenAI, we also refactored the app to use the Python tool chain with OpenAI. This allowed us to leverage the power of OpenAI's GPT model in a more flexible and customizable manner. By swapping out the app's llm chain with a Python agent and setting up the necessary dependencies, we were able to use Python Prompts and responses in our app.

Conclusion

In conclusion, the large language model app proved to be a powerful tool for answering questions, completing tasks, and generating text. When compared to OpenAI's models, the app provided responses that were on par in terms of coherence and accuracy. The ability to use open source models added an extra level of flexibility and control. By refactoring the app to use the Python tool chain with OpenAI, we were able to extend its capabilities even further. Overall, the app demonstrated the potential of large language models in various applications.

FAQ

Q: Can I use the large language model app for commercial purposes? A: The availability of commercially licensable models depends on the specific open source models you choose. When downloading the models through the GPT-4-All GUI, you will find information about their licensability, guiding you on whether you can integrate them into your startup or business app.

Q: Can I run GPT-4-All on GPU instead of CPU? A: Yes, it is possible to run GPT-4-All on a GPU. This can significantly improve the speed and performance of the models, especially for computationally intensive tasks.

Q: Are there other open source large language models available? A: Yes, besides GPT-4-All, there are other open source large language models available, such as Hugging Face's models. However, the performance and compatibility may vary, so it is recommended to thoroughly test and evaluate them for your specific use case.

Q: Can I contribute to the development of the large language model app? A: Yes, the large language model app is open source, and contributions are welcome. You can find the code and instructions on how to contribute on the GitHub repository linked in the article.

Q: How can I use the large language model app in my own projects? A: You can use the large language model app by following the detailed instructions provided in the GitHub repository. The repository includes the necessary code and dependencies to get the app up and running on your machine.

Q: Can the large language model app execute Python code? A: Yes, by leveraging the Python tool chain with OpenAI, the large language model app can execute Python code. This allows for more dynamic and interactive applications.

Q: Can the large language model app generate responses in other languages? A: The large language model app's default behavior is in English. However, with appropriate training, it is possible to fine-tune the model to generate responses in other languages. This requires additional language-specific datasets and preprocessing.

Q: How can I fine-tune the large language model app for my specific use case? A: Fine-tuning the large language model app involves training it on a domain-specific dataset. This dataset should be representative of the tasks and text inputs you expect the model to handle. Fine-tuning requires expertise in machine learning and natural language processing.