Unveiling the Code Llama Models and Their Fine-Tuned Alternatives

Table of Contents

1. Introduction
2. What is Code Llama?
3. Types of Code Llama Models
   • Base Model
   • Instruct Model
   • Python Model
4. Model Sizes
   • 7 Billion
   • 13 Billion
   • 34 Billion
5. Benchmark Performance
   • Code Llama Python vs GPT 3.5
   • Code Llama Python vs GPT 4
   • Code Llama vs Palm-Coder Models
   • Code Llama vs GitHub COPILOT
   • Code Llama vs StarCoder
6. Training Methodology
   • Infilling Objective
   • Training with Special Tokens
   • Long Context Fine Tuning
7. Responsible Use of Code Llama
   • License & Commercial Use
   • Responsible Use Guide
   • Adversarial Prompts
8. Fine Tuned Models
   • Wizard Coder
     • Dataset and Methodology
     • Benchmark Performance
   • Phind Model
     • Dataset and Methodology
     • Benchmark Performance
9. Availability of Datasets
10. Future Possibilities
11. Conclusion

👨‍💻 Code Llama: The Ultimate Programming AI Companion

In recent days, the tech community has been abuzz with the release of Meta's Code Llama model. This highly anticipated AI, which is a fine-tuned version of LLaMA-2, has captured the attention of developers and programmers worldwide. In this article, we will dive into the intricacies of Code Llama, explore its capabilities, and compare it to other existing models. Let's embark on this coding adventure together!

1. Introduction

Code Llama has caused quite a stir in the AI community, with rumors circulating for weeks prior to its official release. In this article, we will not only delve into the original Code Llama model but also explore the various fine-tuned versions that have emerged since then. Excitingly, these fine-tuned models, developed by different groups including WizardLM, offer substantial improvements over the base version. Join us as we examine the features, benefits, and benchmarks of each incarnation of Code Llama.

2. What is Code Llama?

Code Llama is essentially an advanced iteration of LLaMA-2, which has been further trained and fine-tuned exclusively for code-related tasks. To gain access to the Code Llama models, individuals are required to re-opt in and download them from the Hugging Face platform. These models come in three variants: the base model, the instruct model, and the Python model. Each variant serves a specific purpose, catering to diverse programming needs.

3. Types of Code Llama Models

3.1 Base Model

The base model of Code Llama is a fine-tuned version of the original LLaMA-2 model. It serves as the foundation for the subsequent specialized models and provides a solid starting point for developers seeking advanced code generation capabilities.

3.2 Instruct Model

The instruct model is designed specifically for tasks that involve requesting the AI to write custom functions or perform specific coding actions. With the instruct model, developers can effortlessly communicate their coding requirements and obtain accurate and tailored responses.

3.3 Python Model

As the name suggests, the Python model of Code Llama is solely dedicated to Python programming. This model has been fine-tuned to excel in Python-related tasks, making it an invaluable tool for Python developers.

4. Model Sizes

Code Llama offers a range of model sizes to cater to different computational needs. Currently, Meta has released models in three sizes: 7 billion, 13 billion, and 34 billion. It is worth noting that the absence of a 70 billion model raises questions about its potential availability in the future.

5. Benchmark Performance

Code Llama models have demonstrated impressive benchmark performance, particularly in comparison to existing models such as GPT 3.5. While the Python variant of Code Llama outperforms GPT 3.5, it still falls short when compared to the powerful GPT 4. Additionally, Code Llama models have shown superior performance compared to other code generation models like PaLM-Coder and GitHub Copilot.

6. Training Methodology

The training methodology employed for Code Llama is based on an infilling objective. This technique, similar to those used in BERT and T5 models, involves removing a section of code and training the model to predict the missing portion. By utilizing special tokens to mark the beginning and end of the code segment, the model can accurately infer the missing code. This approach enables Code Llama to perform sophisticated code completion tasks.

7. Responsible Use of Code Llama

Meta has emphasized responsible use of Code Llama and has outlined guidelines for commercial utilization of the models. Users are encouraged to refer to the responsible use guide, which covers aspects such as data preparation, fine-tuning, policy definition, evaluation, and performance improvement. It is important to note that Code Llama is provided without guarantees, and thorough testing and evaluation are recommended.

8. Fine Tuned Models

8.1 Wizard Coder

One standout example of fine-tuning Code Llama is the Wizard Coder model developed by the team behind WizardLM. Leveraging their Evol Instruct methodology, they created an extensive dataset for training purposes. Subsequently, they fine-tuned the LLaMA-2 code model to achieve remarkable results. Wizard Coder demonstrates strong performance in benchmark evaluations, surpassing many other code generation models in terms of human evaluation scores.

8.2 Phind Model

Phind, a startup specializing in coding assistance, has also fine-tuned the Code Llama 34 billion model. Their proprietary dataset consists of 80,000 high-quality programming problems and solutions, focusing on code completion examples. While their claims of outperforming GPT 4 on human evaluation should be taken with caution, their model presents an interesting alternative for developers seeking AI-driven pair programming experiences.

9. Availability of Datasets

At Present, there is limited availability of datasets for fine-tuning Code Llama. The datasets used by Wizard Coder and Phind have not been publicly released. However, independent developer Nick Rosh has created an 80,000-example dataset based on the Evol Instruct code dataset. This dataset, along with the accompanying code for dataset creation, provides a valuable resource for those interested in creating their own fine-tuned models.

10. Future Possibilities

The astounding success of the Code Llama models raises intriguing prospects for the future. Could fine-tuning GPT 3.5 with similar datasets yield even better results than the current GPT 4 model? As the field of AI-driven coding continues to advance, exploring such possibilities holds great potential for further advancements and improvements.

11. Conclusion

Code Llama, the highly anticipated programming AI companion, has surpassed expectations with its exceptional capabilities. Through fine-tuned models like Wizard Coder and Phind, developers now have access to powerful tools that can significantly enhance their coding experience. While datasets for fine-tuning remain limited, the community's ongoing exploration and experimentation promise to unlock new possibilities in the world of AI-based programming assistance.

We hope this comprehensive exploration of Code Llama and its fine-tuned models has piqued your interest and sparked new ideas for your coding journey. Join us as we continue to unravel the incredible potential of AI in programming.

Resources:

• Hugging Face
• Wizard Coder Paper
• Phind Website
• Nick Rosh Dataset