Llama 3 vs. GPT-4: Coding, Reasoning, and Math Benchmarks Reveal Surprising Results

You can get started with the Llama 3 model in the following ways:

1. Try the Demos with Hugging Chat: You can access the 70 billion parameter Llama 3 instruct model and start chatting with it right away on the Hugging Chat platform.

2. Use on Meta AI Spaces: You can also test out the 8 billion parameter Llama 3 model on the Meta AI Spaces platform.

3. Explore Other Avenues: There are other platforms like Anthropic's AI Studio and many others where you can try out the Llama 3 model.

To get started, you can check the links provided in the description below. The author also mentioned that they will be making another video showcasing how to install the Llama 3 model, including the uncensored version, so be sure to stay tuned for that.

To assess Llama 3's reasoning capabilities, we tested the 8 billion parameter model and the 70 billion parameter model on their ability to explain the theory of relativity in simple terms for an 8-year-old.

The 8 billion parameter model provided a concise and engaging explanation, using relatable analogies and a storytelling approach to effectively convey the core concepts of relativity. The response demonstrated a good level of simplicity, clarity, and understanding, making it well-suited for an 8-year-old audience.

Similarly, the 70 billion parameter model also delivered a straightforward and accessible explanation of Einstein's theory. While adopting a more direct approach compared to the 8 billion model, the response still managed to effectively illustrate the key principles of relativity using examples like throwing a ball on a moving train. The explanation focused on the interconnectedness of time and space, further reinforcing the model's reasoning capabilities.

Both models performed admirably in this reasoning task, showcasing their ability to break down complex scientific concepts into simple, understandable terms. The 8 billion parameter model's storytelling approach may have slightly edged out the 70 billion model in terms of maintaining the attention and engagement of an 8-year-old, but the overall quality of the explanations was impressive for both models.

These results demonstrate Llama 3's strong reasoning skills, which can be further tested across a variety of challenging problem-solving and conceptual tasks. The model's performance in this assessment suggests its potential to excel in real-world applications that require clear, logical reasoning and the ability to convey complex ideas in an accessible manner.

Both the 8 billion and 70 billion parameter Llama 3 models demonstrated impressive Python coding abilities. When presented with a challenging problem to find the maximum profit that can be obtained by buying and selling a stock at most twice, the models were able to provide step-by-step solutions.

The 8 billion parameter model was able to correctly calculate the maximum profit of $6, even though the function it returned had a profit of $3. The model was able to explain its reasoning and approach in a clear and concise manner.

The 70 billion parameter model went a step further, not only getting the correct maximum profit of $6, but also providing a more detailed and comprehensive explanation of the solution. It outlined the specific script and approach it used to arrive at the final answer.

When tasked with creating a complete Snakes and Ladders game in Python using Pygame, the 70 billion parameter Llama 3 model was able to generate the full working code, including the game board and functional characters. This is a significant achievement, as other language models often struggle to produce operational code for complex games.

Overall, both Llama 3 models demonstrated exceptional Python coding skills, showcasing their ability to solve complex programming problems and generate functional code. The 70 billion parameter model, in particular, stood out with its more detailed explanations and its capability to create a fully working game application.

The Llama 3 model showcased impressive capabilities in generating functional code for a Snakes and Ladders game using PyGame. Unlike other language models that often struggle to produce runnable code, the Llama 3 model was able to generate a complete Python script that successfully displayed the game board and allowed for character movement.

When prompted to create a Snakes and Ladders game in Python with PyGame, the Llama 3 model not only generated the necessary code but also ensured that the game was fully operational. The generated code included the creation of the game board, the implementation of character movement, and the integration of PyGame components to bring the game to life.

This demonstration highlights the Llama 3 model's strong capabilities in the realm of game development. The model's ability to generate functional, runnable code sets it apart from other language models, which often struggle to produce code that can be executed without significant manual intervention or debugging.

The successful generation of the Snakes and Ladders game showcases the Llama 3 model's potential in various game development tasks, such as creating prototypes, implementing game mechanics, and even developing complete game projects. This capability can be particularly valuable for developers, game designers, and hobbyists who are looking to leverage the power of large language models in their game development workflows.

Both the 8 billion and 70 billion parameter Llama 3 models demonstrated strong capabilities in solving challenging mathematical problems.

When presented with a problem to find the maximum profit that can be obtained by buying and selling a stock at most twice, the 8 billion parameter model was able to provide a step-by-step solution. It correctly calculated the maximum profit of $6, even though the function it returned only showed a profit of $3. The model was able to break down the problem and explain its reasoning effectively.

The 70 billion parameter model also solved the same problem, and its response provided an even more comprehensive explanation. It not only arrived at the correct maximum profit of $6, but also detailed the specific steps and logic used to reach that solution. The 70 billion model's explanation was more polished and better articulated compared to the 8 billion model.

Furthermore, when tasked with creating a Python script to implement the classic Snakes and Ladders game using Pygame, the Llama 3 models were able to generate functional code. Unlike other language models that often struggle to produce runnable code, both the 8 billion and 70 billion parameter Llama 3 models were able to create a working game implementation, complete with a graphical board and game mechanics.

These results demonstrate Llama 3's strong mathematical reasoning capabilities and its ability to translate abstract problems into working code solutions. The models' performance on these challenging tasks highlights their potential to be valuable tools for a wide range of applications, from problem-solving to software development.

In conclusion, the Llama 3 model, both the 8 billion parameter and 70 billion parameter versions, have demonstrated impressive capabilities across various benchmarks and tasks.

The models were able to provide clear and concise explanations of the theory of relativity, tailored for an 8-year-old's understanding. Both models showcased strong reasoning abilities, effectively breaking down the complex concepts into relatable analogies.

When tasked with solving a challenging Python coding problem, the models were able to generate the correct solution, with the 70 billion parameter model providing a more detailed and comprehensive explanation of the approach.

Furthermore, the models were able to generate a functional Snakes and Ladders game in Python, including the game board and functional characters. This showcases the models' strong code generation capabilities, outperforming other language models in this regard.

The models also demonstrated proficiency in mathematical problem-solving, providing accurate solutions and detailed explanations of the underlying concepts.

Overall, the Llama 3 models have proven to be highly capable, outperforming many proprietary models in various benchmarks and tasks. As the 400 billion parameter model is released, it will be exciting to see how it further pushes the boundaries of open-source language model performance.