An AI developer team wants to fine-tune an open-weight model to have exceptional performance on...

To optimize forcode generation performanceandhosting costs, a Generative AI engineer must look at specific metrics.

Big Code Models Leaderboard (A):This is the industry-standard benchmark for code-specific LLMs (like StarCoder or CodeLlama). It measures performance on tasks like HumanEval and MBPP, providing a direct indicator of how well the model handles programming logic.

Number of model parameters (B):This is the primary driver of hosting costs. Larger models (e.g., 70B) require more GPU memory (VRAM) and more expensive compute instances (like A100s/H100s) than smaller models (e.g., 7B or 13B). To minimize costs, the team should look for the smallest model that achieves a high score on the Big Code Leaderboard.

Note: MTEB (C) is for embeddings, and Chatbot Arena (D) is for general-purpose chat, neither of which is the primary metric for specialized code generation fine-tuning.