Is CODE LLAMA Really Better Than GPT4 For Coding?!

Name: Is CODE LLAMA Really Better Than GPT4 For Coding?!
Uploaded: 2023-08-30T14:02:06.000Z
Duration: 20 min 21 s

Introduction and Open Source Model Loading

The transcript begins with excitement about an open source model, Code Llama, beating GPT4 in a coding challenge. The model is introduced as an AI tool for coding built on top of the Llama model.

Code Llama beats GPT4 in a coding challenge.

Code Llama is an open source model for coding based on the Llama model.

Comparison and Testing

The speaker discusses plans to compare Code Llama directly to GPT4 through various tests. They mention that Code Llama has already been instruction tuned and express their excitement about potentially finding a better alternative.

Plans to compare Code Llama to GPT4 through testing.

Mention of instruction tuning for Code Llama.

Excitement about finding a potential better alternative.

Introduction to Code Llama

The speaker introduces a blog post by Meta that introduces Code Llama as an AI tool specifically designed for coding. They highlight that it is built on top of the recently released Llama 2 model and can be used for both research and commercial purposes.

Introduction of Meta's blog post introducing Code Llama.

Mention of Code Llama being built on top of the Llama 2 model.

Highlighting that it can be used for research and commercial purposes.

Parameters and Training Data

The speaker discusses the parameters and training data used for training different versions of Code Llama. They mention that there are models with 7 billion, 13 billion, and 34 billion parameters trained with 500 billion tokens of code-related data.

Discussion of different versions of Code Llama with varying parameters.

Mention of training data consisting of 500 billion tokens of code-related data.

Performance Comparison

The speaker mentions that Code Llama 34b Python achieved a higher pass rate on human evaluation (69.5%) compared to GPT4 (67%). They express the need for real-world tests to further evaluate the performance.

Mention of Code Llama 34b Python achieving a higher pass rate on human evaluation compared to GPT4.

Emphasis on the importance of real-world tests for performance evaluation.

Setting Up Testing Environment

The speaker demonstrates two browser windows, one with Code Llama loaded and another with GPT4, for comparison testing. They mention that while GPT4 is easier to set up through a website, Code Llama can be installed locally for free.

Demonstration of browser windows with Code Llama and GPT4.

Mention of ease of setup for GPT4 through a website.

Highlighting that Code Llama can be installed locally for free.

Test Setup and Parameters

The speaker explains the setup used for testing, including using the xlama HF model loader and setting token limits and temperature values. They mention using a system prompt, user message, and assistant output as part of the testing process.

Explanation of test setup using xlama HF model loader.

Mention of token limits and temperature values used.

Description of system prompt, user message, and assistant output in testing.

First Test - Outputting Numbers 1 to 100

The first test involves writing Python code to output numbers 1 to 100. Both Code Llama and GPT4 are expected to pass this test easily.

First test: writing Python code to output numbers 1 to 100.

Code Llama provides a one-liner solution for the task.

GPT4 also provides a solution using a for loop.

Test Results - Outputting Numbers 1 to 100

The speaker confirms that both Code Llama and GPT4 successfully output numbers 1 to 100 when tested in Visual Studio Code.

Confirmation that both Code Llama and GPT4 passed the test of outputting numbers 1 to 100.

Second Test - Writing the Game Snake in Python

The second test involves writing Python code for the game Snake using Pygame. Code Llama provides a basic outline due to token limitations, while GPT4 is expected to provide a more extensive solution.

Second test: writing the game Snake in Python using Pygame.

Code Llama provides a basic outline due to token limitations.

Expectation of GPT4 providing a more extensive solution.

Test Results - Writing the Game Snake in Python

The speaker confirms that Code Llama successfully generates code for the game Snake within the token limit. However, there are some issues with infinite snake growth. They express confidence that further iterations on the prompt would yield better results.

Confirmation that Code Llama generated code for the game Snake within token limit.

Mention of issues with infinite snake growth in the generated code.

Confidence in improving results through prompt iterations.

Conclusion

Code Llama, an open source model built on top of the Llama model, has shown promising performance in coding challenges and comparisons with GPT4. While it successfully handles basic tasks like outputting numbers, there are still some limitations and room for improvement, as seen in the game Snake prompt. Further testing and iterations may help enhance its capabilities.

Setting up the Environment

The speaker discusses setting up the coding environment and demonstrates running code in Visual Studio Code.

Opening and Running Code

The speaker mentions having difficulty opening and running the code initially.

After some troubleshooting, they successfully open and run the code in Visual Studio Code.

The snake game runs correctly, with the snake growing when it eats food.

Testing Code Challenges

The speaker tests code challenges from a website called pythonprinciples.com using both Code Llama and GPT-4 models.

Capital Indexes Challenge

The speaker explains the challenge of finding capital letters in a string and returning their indexes.

They paste the code into both Code Llama and GPT-4 models.

Both models pass the challenge successfully.

All Equal Challenge

The speaker introduces a more intermediate challenge of checking if all elements in a list are the same.

They test this challenge using both Code Llama and GPT-4 models.

While Code Llama passes, GPT-4 fails to return the expected result.

Format Number Challenge

The speaker presents a challenge of converting a non-negative number to a string with commas as thousand separators.

Both Code Llama and GPT-4 models successfully solve this challenge.

Expert Level Challenges

The speaker attempts expert level challenges from another website but encounters difficulties with both models failing to solve them correctly.

Refactoring Code Examples

The speaker explores refactoring code examples using both Code Llama and GPT-4 models.

Refactoring Example 1 - Codelama

The original code is provided along with its refactored version for testing purposes.

Both versions pass when tested individually.

Refactoring Example 2 - GPT-4

The speaker asks GPT-4 to refactor a given code, but it suggests organizing functions under a class instead.

The refactored code provided by GPT-4 is longer than expected and does not meet the requirements.

Refactoring Example 3 - Code Llama

The speaker asks Code Llama to refactor a given code, but it fails to provide any output.

This attempt is considered a failure, and the speaker seeks suggestions for testing these models with code.

Conclusion

The speaker expresses their impression of Code Llama's performance compared to GPT-4 in coding challenges.

Code Llama performs well against GPT-4 and even outperforms it in one challenge.

The speaker acknowledges the significant progress made in coding capabilities and invites viewers to share ideas for further testing.