TuneAhead Predicts LLM Fine-Tuning Performance to Optimize Resource Use

Fine-tuning large language models (LLMs) is a resource-intensive and often unpredictable process, where performance is highly sensitive to data quality and hyperparameter choices. This can lead to wasted computational resources or even degraded model performance if not managed carefully. To address this, the TuneAhead framework offers a solution for predicting fine-tuning performance before a full training run is initiated. It works by encoding each potential fine-tuning run into a meta-feature vector, which combines static descriptions of the dataset with dynamic features obtained from a brief, standardized probe. A predictor then uses these features to generate performance estimates. TuneAhead also incorporates SHAP-based attributions, providing interpretable diagnostics that highlight which specific features are most influential in the prediction. In extensive tests involving over 1,300 fine-tuning runs on Qwen2.5-7B-Instruct, TuneAhead consistently outperformed other baseline methods. On a separate test set, it achieved a Root Mean Squared Error (RMSE) of 1.47 percentage points, with 95.1% of predictions falling within a +/-3 percentage point margin of the actual score. These accurate predictions enable organizations to implement "go/no-go" screening policies, significantly reducing unnecessary full fine-tuning efforts while ensuring promising runs are pursued.