CodeLlama 70B: Outperforming GPT-4, Unleashing the Power of Opensource Coding LLM
Just yesterday we had discussed the release of Meta AI's new large language model, CodeLlama 70 billion parameter model. It's the best open source coding large language model that is actually out there. Now, as promised, I'm going to be covering this new large language model further in detail throughout today's video as we uncover its capabilities. This is something that you can actually download right now, and I have actually created a video showcasing this previously on yesterday's video. So if you're interested in that, take a look at the link in the description below, which will show you how you can get started with this. Now, just to give you a little bit more context before we go further into the video, CodeLlama 70 billion parameter model is absolutely insane because it was actually able to score a 67.8 on the human evalve benchmark, which is something that evaluates a large language model's coding capability. It's something that even outranks GPT-4 in this category. This is not only the best open source coding-based large language model, but it's the best performance-per-coding large language model compared to even closed-source models. Now, with the release of this new model, Meta AI released three variations of CodeLlama. The great part is that it's all free and it's for research purposes as well as commercial use case. Firstly, you have the variation of CodeLlama 70 billion, which is the foundational code model. Secondly, you have CodeLlama 70 billion, which is focusing more on Python and specialized in different types of metrics within the Python language. Lastly, you have the CodeLlama 70 billion instruct model, which is a fine-tuned version for understanding natural language instructions. This is the state-of-the-art large language model that's capable of generating code and natural language about code from both code and natural language prompts. Now, throughout today's video, we're going to be going a little bit more in depth on CodeLlama by exploring the capabilities further in detail, showcase the evaluation, and so much more. So, with that thought, guys, stay tuned and let's get straight into the video.
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Introduction to CodeLlama 70 Billion Model
Hey, what is up, guys? Welcome back to another YouTube video at the World of AI. In today's video, we're going to be taking a look at CodeLlama 70 billion parameter model. This is Meta AI's latest release of its original CodeLlama model, and it stands out to be the largest and most high-performing model within the CodeLlama family. It actually does quite well against many other sorts of models in the open-source field as well as the closed source field. Now, we talked about this at the start, but this is a model that comes in three variations. You have CodeLlama, which is the base model, CodeLlama Python, and CodeLlama Inststruct. This is something that you can actually use right now, and you can even download it with the video that I showcased previously yesterday. Now, these variations are different types of models that specialize in different cases, and it's quite amazing as to how they released this with the 70 billion parameter model size. It has the ability to generate code quite well and articulate natural language about code. Now, I'm going to go a little bit more in-depth on how each of the three variations basically work, but before we even get to that, I want to mention something about this foundational model.
CodeLlama Model Variations
We can see that there are four different variations of the original Llama model. We have the 7 billion, 13 billion, 34 billion, as well as the 70 billion. Now, the great thing is that it all compiles to a 500 billion tokens, 1 trillion for the 70 billion parameter model, and we can see it's split into two different areas. We have the Python code training, the long context fine-tuning, as well as the long context fine-tuning, which is representing 20 billion tokens. This is also the Instruct model, which is being fine-tuned further with 5 billion tokens, and this is the CodeLlama Instruct model, which is feasible for the 70 billion parameter model. And you can see that the CodeLlama is also usable with this long context fine-tuning with the 70 billion tokens, and we also have the CodeLlama Python model with 70 billion tokens with the long context with 20 billion tokens, as well as the Python code training that was originally sent from the foundational models.
Evaluation of CodeLlama's Performance
The evaluation of CodeLlama's performance involves testing it against two widely recognized coding benchmarks. You have the Human Evaluation as well as the Mostly Basic Python Programming (MBPP). The Human Evaluation Benchmark assesses the model's ability to complete code based on Docstring, while the MBPP benchmark evaluates the model's capability to write code based on a description. We can see that CodeLlama demonstrates superior performance compared to existing open-source code-specific large language models, as well as in comparison to GPT-4. It even outperforms it. We can see that GPT-4 recorded a 67 score on human evaluation, passing at 1. If we are to compare it to CodeLlama with the 70 billion parameter model, we can see that it surpassed it, as well as many of the other open-source and closed-source models that are there. And this just goes to show that this is something that demonstrates superior performance.
Accessing and Using CodeLlama Model
You can also download all of these models with the different variations on Hugging Face and also request weights, as well as other different packages that are needed for it to be functional, on their MetaRequest site. You can just simply fill out the form over there, click on the CodeLlama, and you're going to be able to access it within a couple of days if you're approved. And there's also going to be more people fine-tuning it, adding their own quantization methods for it, as well as different types of packages that are fine-tuned with other models. And this is going to be something that you should definitely take a look at on Hugging Face as more models are being released. I basically already made a video showcasing how you can install this, so it's going to be super easy to find different models with the CodeLlama 70 billion and instruct in this case. If you are looking for models that have been fine-tuned with this original model, you can just simply type in CodeLlama 70 billion, and if you click search, you're going to be able to find various other models in that category. So, make sure you check them out on Hugging Face.
The Power of CodeLlama Large Language Model
Now, for the people who do not know what this large language model is, it's a version of Llama 2, which is specifically specializing in code generation. And it has been trained extensively on code-specific datasets. It's something that's able to generate code and articulate natural language about the code based on different prompts in code and natural language. It's available in various different sizes, and it supports programming languages like Python, C++, Java, PHP, TypeScript, and so many others. It supports all of these various programming languages in all of the variations, and it's quite good in different types of capabilities for code completion tasks. Now, if you are to take a look at the 34 billion, as well as this new 70 billion parameter model, it provides superior results in various different categories within the code domain. CodeLlama is actually able to handle up to 100K tokens of context, which is beneficial for debugging in larger code bases. Now, we saw that there are two different variations. We have the CodeLlama Python, which is focused more on the Python language, as well as CodeLlama Instruct, which is a fine-tuned method for understanding natural language instructions. This caters to specific needs, but it's quite awesome to see that this is a great model that can do various different things in various code-related tasks. This is something that I truly recommend that if you have the right tech, you should definitely start playing around with it because only a few selected people can actually run this due to the computation power that is required to run this, so this actually cuts off a lot of users from accessing it.
Conclusion
With all these exciting features and capabilities, CodeLlama 70 billion parameter model stands out as an exceptional open-source large language model for coding. It has surpassed other models, including GPT-4, in terms of performance and offers a range of variations to cater to different programming languages and specific needs. As further development continues, we can expect to see even more impressive models and advancements in the field of coding. If you're interested, I highly recommend checking out the models on Hugging Face and exploring the possibilities. Stay up to date with the latest AI news by following us on Patreon, Twitter, and subscribing to our YouTube channel. Thank you for watching, and have an amazing day!
FAQs
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Can I use CodeLlama for commercial purposes?
Yes, Meta AI has made CodeLlama available for both research purposes and commercial use cases. You can download and utilize the models based on your requirements.
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What programming languages does CodeLlama support?
CodeLlama supports various programming languages, including Python, C++, Java, PHP, TypeScript, and many others. It offers specialized variations for different languages as well.
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Is CodeLlama free to use?
Yes, all variations of CodeLlama are free to use. Meta AI provides these models as open source and encourages research and development in the field of coding.
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Where can I access the CodeLlama models?
You can access the CodeLlama models on Hugging Face. Simply search for "CodeLlama 70 billion" or the specific variation you're interested in, and you will find a variety of models to choose from.
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Can I fine-tune CodeLlama with my own data?
Yes, Meta AI allows users to fine-tune the CodeLlama models with their own data and add their own quantization methods to enhance the performance and capabilities of the models.
