Choosing the Right LLM for Your Needs - Key Considerations

Consider the key factors when selecting a Large Language Model (LLM) for your project.

TL;DR ⏱️

Key Considerations:

📊 Benchmark Performance: Consider the model's performance on relevant benchmarks known from Open LLM Leaderboard and especially Arena Elo. https://lnkd.in/eSkeAUV7
📜 License: Ensure the license aligns with your project's requirements and complies with any regulatory restrictions. https://lnkd.in/e8V-eMCh
📊 Model Size: Consider the trade-off between model size and performance for your specific use case. https://lnkd.in/egZt7BmJ
🔄 Alignment: Evaluate the alignment process and whether it's transparent, as this can impact the model's performance and reliability. https://lnkd.in/eViiEyqp
🔍 Transparency of Training: Look for models with transparent training data and methods to ensure you understand how the model was trained.
💸 Inference Costs: Assess the model's inference costs and consider the trade-off between performance and cost. https://lnkd.in/etSajZZc
📚 Context Size: Consider the model's context size and whether it's suitable for your specific use case.
🤝 Compatibility: Evaluate whether the model is compatible with SOTA libraries like transformers and whether it's easily integrable into your workflow.
📈 Scaling Efficiency: Assess the model's scaling efficiency or e.g. it has full quadratic complexity with more input tokens.
🌎 Multilingualism: Evaluate the model's multilingual capabilities it's effect for your specific use case. https://lnkd.in/eeVsG99M

What is your importance-order of LLM features you look at? Which criteria do you miss in this list or which should be ranked higher?
Within our Projects Comma Soft AG these are some of the major criteria we look at when we are selecting GenAI models like LLMs.
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