Is CODE LLAMA Really Better Than GPT4 For Coding?!

Matthew Berman
30 Aug 202310:20

TLDRIn a detailed comparison, CodeLama, an open-source AI coding assistant built on Meta's Llama 2 model, is pitted against GPT-4. The test involves coding challenges, including creating a snake game in Python and refactoring code. CodeLama impresses by loading the game, a feat no other open-source model has achieved, and outperforms GPT-4 in certain tasks, despite some shared failures in more complex challenges. The video concludes with the presenter's admiration for CodeLama's capabilities and its potential to revolutionize open-source AI in coding.


  • ๐Ÿš€ CodeLama, an open-source AI coding assistant model, has been released by Meta and is based on the Llama 2 model.
  • ๐Ÿ† CodeLama outperformed GPT-4 in certain coding challenges, showcasing its potential as a competitive tool in the coding realm.
  • ๐Ÿ’ก CodeLama is available in different versions (7 billion, 13 billion, and 34 billion parameters) to fit various hardware capabilities.
  • ๐Ÿ“ˆ The 34 billion parameter version of CodeLama achieved a higher pass rate on human eval compared to GPT-4 (69.5% vs 67%).
  • ๐Ÿ“Š CodeLama and GPT-4 were tested on basic to expert-level coding problems, with varying results in problem-solving.
  • ๐ŸŽฎ In a notable achievement, CodeLama successfully loaded and ran a basic snake game in Python using the pygame library.
  • ๐Ÿ” The testing included a range of tasks from outputting numbers to refactoring code and handling expert-level coding challenges.
  • ๐Ÿ“‹ Both models were able to solve a formatting function challenge, but failed when it came to the longest alternating substring problem.
  • ๐Ÿ”„ CodeLama demonstrated the ability to refactor code effectively, while GPT-4 suggested organizing functions under a class for refactoring.
  • ๐Ÿค– The competition between CodeLama and GPT-4 highlights the ongoing advancements in AI coding assistance and open-source contributions.
  • ๐ŸŒ The video script suggests that the community can provide further ideas for testing these AI models with Python code.

Q & A

  • What is the significance of CodeLama beating GPT-4 in the challenge?

    -The significance lies in the fact that CodeLama, an open-source model, managed to outperform GPT-4, which is known for its advanced capabilities. This indicates a major advancement in the field of AI and open-source technology, showcasing that open-source models can compete with proprietary, high-end models in complex tasks such as coding.

  • What is the basis of CodeLama's development?

    -CodeLama is built on top of the Llama 2 model, which was recently released by Meta. It has been fine-tuned specifically for coding tasks, making it a specialized tool for developers.

  • How does CodeLama compare to GPT-4 in terms of accessibility and cost?

    -CodeLama is available for free for both research and commercial use, whereas GPT-4 is a paid service. This makes CodeLama more accessible to a wider range of users, especially those who are cost-sensitive or working on open-source projects.

  • What are the different versions of CodeLama available?

    -CodeLama is available in three versions based on the number of parameters: 7 billion, 13 billion, and 34 billion. The larger the parameter count, the more complex and potentially capable the model is, though it also requires more resources to run.

  • How was CodeLama's performance evaluated in the blog post?

    -CodeLama's performance was evaluated through human eval, where the 34B version of CodeLama and CodeLama 34B Python achieved 67.6% and 69.5% pass rates, respectively, which is slightly better than GPT-4's 67%.

  • What was the first test conducted on both CodeLama and GPT-4?

    -The first test was to write Python code to output numbers 1 to 100. Both models were expected to handle this task easily, and they both passed the test.

  • What issue was found with the snake game code provided by CodeLama?

    -The snake game code provided by CodeLama had an issue where the snake grew indefinitely, and it did not end when it went into itself or the walls, which is not the correct behavior for the game.

  • How did GPT-4 perform on the 'all equal' challenge?

    -GPT-4 failed the 'all equal' challenge. It provided a function that incorrectly returned false when it should have returned true, indicating that not all elements in the list were the same.

  • What was the outcome of the 'format number' challenge?

    -Both CodeLama and GPT-4 successfully completed the 'format number' challenge. They provided concise and correct code to add commas as thousand separators to a number.

  • What happened when CodeLama and GPT-4 were given the 'longest alternating substring' challenge?

    -Neither CodeLama nor GPT-4 successfully solved the 'longest alternating substring' challenge. Both models failed to provide a working solution for this expert-level coding problem.

  • How did the refactoring exercise between CodeLama and GPT-4 turn out?

    -CodeLama was able to provide a code example and its refactored version, which both worked as expected. However, when GPT-4 was asked to refactor CodeLama's code, it did not output anything, resulting in a fail for that part of the exercise.



๐Ÿš€ Introduction to CodeLama and Comparison with GPT-4

The paragraph introduces the open-source model CodeLama, which has outperformed GPT-4 in a coding challenge. It discusses CodeLama's concise solution and its potential to be a superior alternative to GPT-4 in coding tasks. The video's aim is to test CodeLama against GPT-4 and compare their performance. CodeLama is built on the Llama 2 model and is fine-tuned for coding, making it free for both research and commercial use. The model operates on a 34 billion parameter model, which can fit on consumer-grade hardware with a top-tier GPU. The video also mentions the availability of smaller quantized versions of CodeLama and the training data it uses. A comparison setup is described, with CodeLama running on RunPod and GPT-4 on a website, highlighting the ease of setup and accessibility of both models.


๐Ÿ“ Coding Challenges and Results

This paragraph details the coding challenges presented to both CodeLama and GPT-4. It starts with basic tasks like printing numbers 1 to 100, which both models accomplish successfully. The paragraph then discusses the creation of a snake game in Python using pygame, where CodeLama provides a working, albeit imperfect, solution within the token limit. GPT-4's response is similar but with a more accurate implementation of the game's mechanics. The paragraph continues with intermediate and expert-level coding challenges from a website, where CodeLama outperforms GPT-4 in an intermediate challenge but both fail in the expert-level challenge. The video concludes with a refactoring task, where CodeLama successfully refactors a given code, but GPT-4's refactoring attempt is less effective.


๐Ÿ† Conclusion and Final Thoughts

The final paragraph wraps up the video by summarizing the performance of CodeLama against GPT-4 in the coding challenges. It highlights that CodeLama held its own and even outperformed GPT-4 in one of the challenges, marking a significant achievement for an open-source model in the coding domain. The video creator expresses surprise and excitement about CodeLama's capabilities and encourages viewers to share their thoughts and suggestions for further testing in the comments section. The video ends with a call to like and subscribe for more content.




CodeLama is an open-source AI tool for coding developed by Meta, built on top of the Llama 2 model. It is fine-tuned specifically for coding tasks and is available in different versions based on the number of parameters, allowing it to be used on various hardware. In the video, CodeLama is compared with GPT-4, demonstrating its capability in coding tasks and even outperforming GPT-4 in certain challenges.


GPT-4 is a reference to an advanced language model, likely a version of OpenAI's Generative Pre-trained Transformer. It is used as a benchmark in the video to compare its coding capabilities with CodeLama. GPT-4 is known for its ability to generate human-like text and perform various language-related tasks, but in this context, it is evaluated specifically for coding challenges.

๐Ÿ’กOpen Source

Open source refers to software or tools whose source code is made publicly available, allowing anyone to view, use, modify, and distribute the software freely. In the context of the video, CodeLama is highlighted as an open-source model, emphasizing its accessibility and collaborative potential for the coding community.


Meta, formerly known as Facebook, Inc., is the parent company of Facebook and other platforms and technologies. In the video, Meta is mentioned as the developer of the Llama model and the creator of CodeLama, showcasing their continued dominance in the open-source AI space.

๐Ÿ’กParameter Model

A parameter model in the context of AI and machine learning refers to a model that has a specific number of parameters, which are the weights and biases that the model learns during training. The number of parameters is indicative of the model's complexity and capacity for learning. In the video, CodeLama is available in versions with 7 billion, 13 billion, and 34 billion parameters, each trained with a large dataset to perform coding tasks.


Quantization in the context of AI models refers to the process of reducing the precision of the model's parameters to save space and computational resources. This allows larger models to run on consumer-grade hardware without the need for top-of-the-line GPUs. In the video, it is mentioned that there are quantized versions of CodeLama that can be used on less powerful hardware.

๐Ÿ’กToken Limit

A token limit refers to the maximum number of tokens, or individual elements of data, that a model can process at one time. This limit can affect the length of the input that the model can handle and, consequently, the complexity of the tasks it can perform. In the video, the token limit is set to 2048 for the XLama HF model loader, which is used to run CodeLama.


In the context of AI language models, temperature is a hyperparameter that controls the randomness or creativity of the model's output. A lower temperature value makes the model more deterministic and likely to produce straightforward, less creative responses, which is often desired in coding tasks where accuracy is paramount.

๐Ÿ’กSnake Game

The snake game is a classic video game where the player controls a snake that grows as it consumes food, with the objective being to avoid the snake hitting its own tail or the walls of the playing area. In the video, the challenge is to write a basic version of this game using Python and the pygame library, which serves as a test of the AI's coding capabilities.

๐Ÿ’กCoding Challenges

Coding challenges are tasks or problems that require writing computer programs to solve specific problems within certain constraints. These challenges are often used to assess a programmer's skills or to train and improve problem-solving abilities. In the video, coding challenges from different difficulty levels are used to test the performance of CodeLama and GPT-4.


Refactoring in programming refers to the process of restructuring existing computer code without changing its external behavior. The goal is to improve the nonfunctional attributes of the software, making it easier to understand, maintain, and extend. In the video, the AI models are given a task to write code that can be refactored and then to refactor the given code, demonstrating their understanding of code structure and optimization.


CodeLama, an open source model, has outperformed GPT-4 in a coding challenge.

CodeLama is built on top of Llama 2, released by Meta and fine-tuned specifically for coding.

Meta's blog post introduces CodeLama as an AI tool for coding, highlighting its capabilities and availability for free use.

CodeLama is based on a 34 billion parameter model, which can fit on consumer-grade hardware with a top-line GPU.

The 34 billion parameter version of CodeLama achieved higher pass rates on human eval compared to GPT-4.

CodeLama is released with 7 billion, 13 billion, and 34 billion parameter versions, all trained with 500 billion tokens of code-related data.

The testing setup includes a comparison between CodeLama and GPT-4, using XLama HF model loader and a specific prompt template.

CodeLama provided a one-liner Python code to output numbers 1 to 100, which was successfully tested.

GPT-4 also correctly generated code for the same task, demonstrating both models' capability in basic coding tasks.

When tasked with writing a snake game in Python using pygame, CodeLama managed to load the game, a first for an open source model.

GPT-4 provided a similar code for the snake game, but with additional functionality such as growing the snake and ending the game upon collision.

In the 'Capital Indexes' challenge, both CodeLama and GPT-4 successfully returned a list of indexes with capital letters in a string.

CodeLama outperformed GPT-4 in the 'All Equal' intermediate challenge, providing a correct function that checks for identical list elements.

GPT-4 failed the 'All Equal' challenge, indicating a specific instance where CodeLama demonstrated superior performance.

Both models successfully passed the 'Format Number' challenge, converting numbers to strings with thousand separators.

Neither CodeLama nor GPT-4 could solve the 'Longest Alternating Substring' expert-level challenge, showing a common limitation.

CodeLama effectively followed instructions to write and refactor Python code, showcasing its understanding of code restructuring.

GPT-4 offered a refactoring suggestion that organized functions under a class, though it was not exactly what was requested.

In a unique test, CodeLama was unable to refactor GPT-4's code, as it did not output any result, indicating a potential issue with the prompt or setup.

The video concludes with the presenter's admiration for CodeLama's performance against GPT-4, marking a significant advancement in open source AI for coding.