Best Open Source LLMs for Code Generation in 2024

The Best Open Source LLMs for Code Generation in 2024

The field of code generation has witnessed groundbreaking advancements in recent years, with open-source language models (LLMs) increasingly rivaling their proprietary counterparts. These models offer unparalleled transparency, adaptability, and opportunities for community-driven improvements. In this blog, we’ll explore the leading open-source LLMs for code generation, focusing on their performance, efficiency, and practical applications.

Why Choose an Open-Source Coding LLM?

Open-source LLMs offer distinct advantages:

• Transparency: Access to model architecture and data allows developers to fully understand the capabilities and limitations of these tools.
• Customizability: Organizations can fine-tune open-source models to meet specific coding requirements.
• Cost-Effectiveness: Open-source solutions are often far more affordable compared to proprietary models.

However, challenges such as consistency, rapid technological evolution, and integration hurdles remain important considerations when implementing these models.

Evaluating Open Source LLMs for Code Generation

Benchmark Results

The following models have been evaluated based on their performance in code editing tasks:

• DeepSeek Coder V2 0724: 73%
• Llama 3.1 405B Instruct: 66%
• Mistral Large 2 (2407): 60%
• Llama 3.1 70B Instruct: 59%
• Llama 3.1 8B Instruct: 38%

Key Models in Detail

DeepSeek Coder V2 0724

DeepSeek Coder V2 stands out as the best open-source LLM for code generation. Released in July 2024, it offers:

• Top-notch performance: Scored 73% on the aider code editing leaderboard.
• Efficiency: Cost-effective, estimated to be 20-50 times less expensive than proprietary competitors.
• Features: Advanced capabilities for large-scale code editing and mathematical reasoning.

Llama 3.1 Family

Meta’s Llama 3.1 models, released in mid-2024, provide versatile options:

• Llama 3.1 405B Instruct: The flagship model with 66% benchmark performance, suitable for extensive code refactoring.
• Llama 3.1 70B Instruct: Mid-sized model scoring 59%, competitive with GPT-3.5 for rapid prototyping.
• Llama 3.1 8B Instruct: A compact model, best for lightweight tasks, scoring 38%.

Mistral Large 2 (2407)

Mistral AI’s offering delivers reliable performance for smaller tasks:

• Score: Achieved 60% on the aider leaderboard.
• Ideal for: Lightweight code editing and specialized tasks.

Conclusion

Which Open-Source LLM Should You Choose?

• Best Overall Performer: DeepSeek Coder V2 0724 excels in cost-efficiency and performance.
• Best for Large-Scale Projects: Llama 3.1 405B Instruct is ideal for large codebases.
• Best for Rapid Prototyping: Llama 3.1 70B Instruct and Mistral Large 2 are efficient for smaller projects.
• Best for Fine-Tuned Applications: Open-source models can be customized for domain-specific coding.

Open-source LLMs are revolutionizing software development, offering flexible, cost-effective, and high-performance solutions. As the field evolves, these models will play an even greater role in democratizing access to AI-powered coding tools.

FAQs

Q: What is the best open-source LLM for cost-effective code generation?
A: DeepSeek Coder V2 0724 offers top-notch performance at a fraction of the cost of proprietary models.

Q: Can open-source LLMs handle large-scale code refactoring?
A: Yes, models like DeepSeek Coder V2 and Llama 3.1 405B Instruct are excellent for large-scale projects.

Q: How customizable are open-source LLMs?
A: Open-source models can be fine-tuned for specific use cases, making them highly adaptable for various industries.

Q: Are open-source LLMs suitable for smaller projects?
A: Yes, compact models like Llama 3.1 70B and Mistral Large 2 are great for rapid prototyping and lightweight tasks.