Is CODE LLAMA Really Better Than GPT4 For Coding?!
TLDRIn a detailed comparison, CodeLama, an open-source AI coding assistant based on Meta's Llama 2 model, is pitted against GPT-4. CodeLama demonstrates impressive performance, solving coding challenges efficiently and even outperforming GPT-4 in certain tasks. The video showcases various coding problems, from simple tasks to complex ones, highlighting the capabilities and potential of CodeLama as a powerful tool for developers.
Takeaways
- 🚀 CodeLama, an open source AI coding assistant model, has been released by Meta and is based on the Llama 2 model.
- 🏆 CodeLama has shown the ability to outperform GPT-4 in certain coding challenges, demonstrating its potential as a competitive tool.
- 💻 CodeLama is available in different versions (7 billion, 13 billion, and 34 billion parameters) to fit various hardware capabilities, including consumer-grade options.
- 📈 The 34 billion parameter version of CodeLama was fine-tuned with an internal find data set and achieved a higher pass rate on human eval compared to GPT-4.
- 📝 Both CodeLama and GPT-4 were tested on their ability to write Python code, refactor code, and solve coding challenges from various difficulty levels.
- 🎮 CodeLama successfully created a basic snake game in Python using pygame, which is considered a significant achievement for an open source model.
- ✅ Both models were able to write a Python function to output numbers 1 to 100 and solve a beginner-level coding challenge from pythonPrincipals.com.
- 🔍 In an intermediate challenge, CodeLama outperformed GPT-4 by correctly identifying all elements in a list that are the same, while GPT-4 failed this test.
- 📉 Both CodeLama and GPT-4 failed to solve an expert-level challenge involving finding the longest alternating substring in a string of digits.
- 🔄 CodeLama and GPT-4 were given a task to write and refactor Python code; both performed well in creating the initial and refactored code.
- 🤖 The video script concludes with the creator expressing surprise and satisfaction at CodeLama's performance, indicating a promising future for open source AI in coding.
Q & A
What is CodeLama and how does it compare to GPT-4 in terms of coding capabilities?
-CodeLama is an AI tool for coding built on top of the Llama 2 model, fine-tuned by Meta specifically for coding tasks. It has been shown to outperform GPT-4 in certain coding challenges, demonstrating its strength in the coding domain. It is available in different versions with up to 34 billion parameters, making it a powerful tool for developers.
How was CodeLama's performance in the test where it was asked to write a Python code to output numbers 1 to 100?
-CodeLama successfully provided a simple one-liner Python code to output numbers from 1 to 100, which worked perfectly when tested in Visual Studio Code.
What was the outcome when CodeLama was tasked with writing the snake game in Python using pygame?
-CodeLama managed to write a basic outline for the snake game within the 2000 token limit. The game loaded and functioned, although with some issues such as the snake growing indefinitely and not ending when hitting itself or the wall.
How did GPT-4 perform in comparison to CodeLama in writing the snake game?
-GPT-4 also provided a code snippet for the snake game, which was very similar to CodeLama's. However, GPT-4's code had a more complete functionality, with the snake growing when eating food and the game ending when the snake hit itself or the wall.
What was the result of the 'Capital Indexes' coding challenge when given to both CodeLama and GPT-4?
-Both CodeLama and GPT-4 successfully passed the 'Capital Indexes' challenge by providing correct Python functions that returned a list of indexes with capital letters in a given string.
In the 'All Equal' intermediate challenge, did both AI models perform equally well?
-Yes, both CodeLama and GPT-4 provided correct Python functions for the 'All Equal' challenge, which checked whether all elements in a list were the same and returned the appropriate boolean value.
What was the outcome of the 'Format Number' challenge for CodeLama and GPT-4?
-Both CodeLama and GPT-4 successfully completed the 'Format Number' challenge by providing concise and correct Python functions that converted a non-negative number to a string with commas as thousand separators.
How did CodeLama handle the 'Longest Alternating Substring' expert level challenge?
-CodeLama attempted to handle the 'Longest Alternating Substring' challenge but failed due to a code error related to string formatting, which indicates that the provided solution was not entirely correct.
What was the result when GPT-4 was given the 'Longest Alternating Substring' challenge?
-GPT-4 also failed the 'Longest Alternating Substring' challenge, as the provided solution did not work when tested, showing that this was a difficult problem for both AI models.
What was the task given to both models regarding refactoring Python code?
-Both models were given a task to write Python code that could be refactored, and then to provide the refactored code. CodeLama followed the instructions correctly, while GPT-4 offered a refactoring that organized the functions under a class, which was not exactly what was requested.
What happened when CodeLama was asked to refactor GPT-4's code?
-CodeLama failed to provide any output when asked to refactor GPT-4's code, which was an unexpected result indicating a possible issue with the setup or the prompt given to the model.
Outlines
🚀 Introduction to CodeLama and its Comparison with GPT-4
The paragraph introduces the CodeLama AI tool for coding, an open-source model developed by Meta that has outperformed GPT-4 in certain challenges. It discusses the capabilities of CodeLama, including its concise problem-solving approach and its basis on a 34 billion parameter model. The video aims to test CodeLama against GPT-4 through various coding tasks and compares their performance. The paragraph also mentions the availability of different versions of CodeLama and the setup process for the tests, including the use of XLama HF model loader and specific settings for the coding tasks.
📝 Coding Challenges and Results
This paragraph details the coding challenges presented to both CodeLama and GPT-4. It begins with simple tasks like writing a Python code to output numbers 1 to 100, and progresses to more complex tasks such as writing the snake game in Python using pygame. The results show that while both models perform well on basic tasks, CodeLama outperforms GPT-4 on intermediate and even expert-level coding challenges, demonstrating its strength in the coding domain. The paragraph also highlights the testing of code refactoring capabilities of both models.
🏆 Conclusion and Final Thoughts
The final paragraph wraps up the video by summarizing the performance of CodeLama against GPT-4. It emphasizes the impressive results of CodeLama, especially in the coding challenges where it outperformed GPT-4. The video creator expresses surprise and excitement over CodeLama's capabilities and its potential as a competitor to GPT-4 in the coding realm. The paragraph concludes with a call to action for viewers to like and subscribe for more content.
Mindmap
Keywords
💡Open Source
💡CodeLama
💡GPT-4
💡Parameter
💡Fine-Tuning
💡Quantized
💡Token
💡Python
💡Pygame
💡Refactor
💡Challenge
Highlights
CodeLama, an open source model, has successfully loaded a game for the first time, marking a significant achievement in the realm of AI coding assistants.
CodeLama managed to beat GPT-4 in a coding challenge, showcasing its potential as a leading open source AI model for coding.
Meta's blog post introduces CodeLama as an AI tool for coding, highlighting its capabilities and potential applications in the field.
CodeLama is built on top of the Llama 2 model, which was recently released and is free for both research and commercial use.
The model is fine-tuned on top of the Llama model specifically for coding, which could make it a strong contender against other AI coding assistants.
CodeLama is based on a 34 billion parameter model, making it possible to fit on consumer-grade hardware with the right GPU.
CodeLama's 34B and 34B Python models achieved higher pass rates on human eval than GPT-4, indicating its effectiveness in coding tasks.
The testing process involved comparing CodeLama and GPT-4 directly, using various coding challenges to evaluate their performance.
Both models were tasked with writing Python code to output numbers 1 to 100, a basic test that both passed successfully.
When asked to write the snake game in Python using pygame, CodeLama provided a basic outline within the 2000 token limit, demonstrating its ability to handle complex tasks.
GPT-4, on the other hand, provided a more complete version of the snake game that actually worked, including features like the snake growing when it eats and ending the game when it hits itself or the wall.
In the 'Capital Indexes' challenge from pythonPrincipals.com, both CodeLama and GPT-4 successfully wrote and passed a function to return indexes of capital letters in a string.
CodeLama outperformed GPT-4 in the 'All Equal' intermediate challenge, showcasing its ability to handle more nuanced coding tasks.
Both models were able to write and pass the 'Format Number' challenge, which involved adding commas as thousand separators to a number.
The 'Longest Alternating Substring' expert level challenge proved to be too difficult for both CodeLama and GPT-4, with neither model providing a working solution.
A unique test involved writing Python code that could be refactored and then having the other model refactor it, showcasing the models' understanding of code structure and improvement.
CodeLama's performance in the refactoring test was impressive, as it followed instructions and provided both the original and refactored code correctly.
GPT-4's refactoring attempt, while not exactly what was requested, still demonstrated an understanding of organizing code under a class, showing adaptability in code structuring.
The video concludes with the creator expressing surprise and admiration for CodeLama's ability to compete with GPT-4 in coding tasks, indicating a promising future for open source AI in coding.
The video encourages viewers to share their thoughts and ideas for further testing the capabilities of these AI models, fostering community engagement and collaboration.
Overall, the video highlights the significant progress in AI coding assistants and the potential for open source models like CodeLama to become strong competitors in the field.