Introduction to the "What LLM to Use?" Project
In the rapidly evolving landscape of developer artificial intelligence (DevAI), choosing the right large language model (LLM) can be a daunting task. These LLMs assist developers in creating software, making their selection critical for efficient and effective coding. The "What LLM to Use?" project aims to simplify this process by providing a comprehensive guide to the most widely-used LLMs, particularly for those involved in coding.
Available LLMs
The DevAI space is home to a plethora of LLMs, but the project focuses on those currently favored by developers. This curated selection includes models both from open-source communities and commercial entities. A visual representation of these LLMs is displayed in an image, and detailed information is available in a CSV file linked within the project repository.
Choosing an LLM for Coding
Decision Factors
Developers must first decide whether they prefer an open-source or a commercial LLM, based on various factors:
- Open-Source LLMs: These are ideal for developers who want to maintain control over their code environment, manage costs, or enhance their models. They can be deployed locally or through a hosted provider.
- Commercial LLMs: These are typically more polished and reliable, allowing for an easier setup, though they may incur higher costs and require code to be shared with external platforms.
Open Source Options
Open-source LLMs are gaining traction, and the project lists some of the most popular, including:
- Code Llama: Developed by Meta, this model specializes in generating and discussing code.
- WizardCoder: Built on Code Llama, it is instruction-tuned for coding tasks.
- Phind-CodeLlama: This model is fine-tuned with a vast dataset, leading current coding model leaderboards.
- Mistral: Excels in both coding and English tasks despite its smaller size.
- StarCoder: Supports over 80 programming languages but lacks instruction tuning.
- DeepSeek Coder: A new entrant reported to perform well on coding benchmarks.
- Llama 2: Although not as effective for code edits, it forms the base for other popular models.
Commercial Options
For developers willing to invest in commercial solutions, the following models are prominent:
- GPT-4: Known for its superior capabilities in code generation, though expensive.
- GPT-4 Turbo: Offers similar capabilities at a reduced cost and speed.
- GPT-3.5 Turbo: More cost-effective than GPT-4, though with less comprehensive suggestions.
- Claude 2: Enhances its performance significantly with more context provided by the user.
- PaLM 2: Developed by Google, offering robust solutions through their API.
Community Contributions
The project encourages community engagement by inviting developers to contribute any missing information or share personal insights. This participatory approach aims to keep the repository updated and relevant, fostering a collaborative environment for DevAI enthusiasts.
To stay informed about future developments in DevAI, interested individuals are invited to subscribe to a monthly newsletter provided by the project team.
By offering these insights into various LLMs, the "What LLM to Use?" project aspires to aid developers in making informed decisions, ultimately optimizing their software development endeavors.