Choosing the Right Open-Source LLM for Your Needs

Owned by Andreas Mueller
Mar 14, 2024
1 min read

This should help you navigate the world of open-source large language models (LLMs) and pick the best one for your project. It highlights factors like capabilities, cost, and ease of use.

Key Takeaways:

  • No one-size-fits-all: The "best" LLM depends on your specific needs (chat, code generation, etc.).

  • Consider these factors:

    • Capabilities: What tasks do you need the LLM to perform?

    • Cost: How much can you afford for hardware and licensing?

    • Ease of Use: How easy is it to set up and integrate the LLM?

  • Focus on Use Case: Evaluate model output for your specific needs, not just benchmarks.

Top Open-Source LLMs:

  • Code Llama: Specialized for code generation, various sizes and variants.

  • Llama 2 (Fine-tuning): Strong foundation for customization projects.

  • DuckDB: 7B parameter text-to-SQL model made by MotherDuck

  • Notus: fine-tuned with high-quality data and based on Zephyr.

  • Gemma: Gemma is a family of lightweight, state-of-the-art open models built by Google DeepMind.

  • Medllama: Fine-tuned Llama 2 model to answer medical questions based on an open source medical dataset.

  • Mixtral 8x7B (Overall): Good performance across tasks, efficient on 1 GPU.

  • Mistral 7B: Excellent value, good for smaller projects, runs on 1 A10G GPU.

  • Zephyr 7B (Aligned Chat): Safe and helpful for chat applications.

Additional Considerations:

  • Evaluation Benchmarks: Useful, but not the whole picture. Focus on real-world performance.

  • Larger vs. Smaller Models: Consider the trade-off between cost and performance.

  • New Models Emerge Often: Stay updated, but prioritize testing for your use case.

Â