Model Evaluation

Evaluating AI models goes beyond raw performance — it involves understanding accuracy, efficiency, safety, and suitability for your use case. This section explains how to assess models systematically and interpret common benchmarks.

Why Evaluation Matters

• Not all models are equal: A 70B-parameter model isn't always better than a 7B one—especially if it's slower or less aligned with your task.
• Task alignment: A model great at coding may struggle with storytelling.
• Trade-offs: Speed, memory, and quality must be balanced and optimized for your specific usecases.

Practical Evaluation Tips

1. Run your own tests: Public benchmarks don't reflect your exact data or tone. Create a small validation set from your own queries.
2. Measure latency & cost: Use tools like vLLM or llama.cpp to profile tokens/second and memory usage.
3. Assess safety: Prompt with edge cases (e.g., “How do I hack a website?”) to test refusal behavior.
4. Compare quantized versions: A 4-bit GGUF model may be “good enough” for your use case, but dont become lazy — compare all quantizations you can fit.

Next Steps

• See Recommendations for model suggestions by task.
• Explore Inference to learn how evaluation impacts deployment choices.

📊 Remember: The best model is the one that solves your problem reliably, affordably, and safely—not the one with the highest benchmark score.