Protocol

Fine-tuning and evaluation wall time, 5 epochs on the Enformer eQTL dataset (Hegde et al. 2025 Table A1)

Wall time to fine-tune for 5 epochs on the eQTL training split and to evaluate on its test split, one row per random seed.

1 evaluation · 6 results

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1 recorded evaluation, 6 metric rows. A comparison chart has not yet been validated for these results. The table retains the individual findings and their sources.

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: NT 500M human reference, fine-tuned 5 epochs, unpruned (Hegde et al. 2025)	Protocol: Fine-tuning and evaluation wall time, 5 epochs on the Enformer eQTL dataset (Hegde et al. 2025 Table A1) Dataset: Enformer eQTL causal-variant dataset, Genomics Long-Range Benchmark version (Hegde et al. 2025)	8.49 Runtime (Evaluation on the eQTL test split after 5-epoch fine-tuning) minute · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source NT 500M human reference, 5-epoch fine-tuning time (Hegde et al. 2025 Table A1) gfm-guide-20261014-protocol-hegde-eqtl-finetune-5-epochs-time Aggregation: Not reported Reaping the Fruits of LLM Pruning: Towards Small Language Models for Efficient Non-Coding Variant Effect Prediction · Table A1, row seed 41, column 'Evaluation Time (Minutes)'
Configuration: NT 500M human reference, fine-tuned 5 epochs, unpruned (Hegde et al. 2025)	Protocol: Fine-tuning and evaluation wall time, 5 epochs on the Enformer eQTL dataset (Hegde et al. 2025 Table A1) Dataset: Enformer eQTL causal-variant dataset, Genomics Long-Range Benchmark version (Hegde et al. 2025)	8.51 Runtime (Evaluation on the eQTL test split after 5-epoch fine-tuning) minute · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source NT 500M human reference, 5-epoch fine-tuning time (Hegde et al. 2025 Table A1) gfm-guide-20261014-protocol-hegde-eqtl-finetune-5-epochs-time Aggregation: Not reported Reaping the Fruits of LLM Pruning: Towards Small Language Models for Efficient Non-Coding Variant Effect Prediction · Table A1, row seed 42, column 'Evaluation Time (Minutes)'
Configuration: NT 500M human reference, fine-tuned 5 epochs, unpruned (Hegde et al. 2025)	Protocol: Fine-tuning and evaluation wall time, 5 epochs on the Enformer eQTL dataset (Hegde et al. 2025 Table A1) Dataset: Enformer eQTL causal-variant dataset, Genomics Long-Range Benchmark version (Hegde et al. 2025)	8.49 Runtime (Evaluation on the eQTL test split after 5-epoch fine-tuning) minute · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source NT 500M human reference, 5-epoch fine-tuning time (Hegde et al. 2025 Table A1) gfm-guide-20261014-protocol-hegde-eqtl-finetune-5-epochs-time Aggregation: Not reported Reaping the Fruits of LLM Pruning: Towards Small Language Models for Efficient Non-Coding Variant Effect Prediction · Table A1, row seed 43, column 'Evaluation Time (Minutes)'
Configuration: NT 500M human reference, fine-tuned 5 epochs, unpruned (Hegde et al. 2025)	Protocol: Fine-tuning and evaluation wall time, 5 epochs on the Enformer eQTL dataset (Hegde et al. 2025 Table A1) Dataset: Enformer eQTL causal-variant dataset, Genomics Long-Range Benchmark version (Hegde et al. 2025)	7.53 Runtime (Fine-tuning, 5 epochs, unpruned model) hour · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source NT 500M human reference, 5-epoch fine-tuning time (Hegde et al. 2025 Table A1) gfm-guide-20261014-protocol-hegde-eqtl-finetune-5-epochs-time Aggregation: Not reported Reaping the Fruits of LLM Pruning: Towards Small Language Models for Efficient Non-Coding Variant Effect Prediction · Table A1, row seed 41, column 'Training Time (Hours)'
Configuration: NT 500M human reference, fine-tuned 5 epochs, unpruned (Hegde et al. 2025)	Protocol: Fine-tuning and evaluation wall time, 5 epochs on the Enformer eQTL dataset (Hegde et al. 2025 Table A1) Dataset: Enformer eQTL causal-variant dataset, Genomics Long-Range Benchmark version (Hegde et al. 2025)	7.5 Runtime (Fine-tuning, 5 epochs, unpruned model) hour · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source NT 500M human reference, 5-epoch fine-tuning time (Hegde et al. 2025 Table A1) gfm-guide-20261014-protocol-hegde-eqtl-finetune-5-epochs-time Aggregation: Not reported Reaping the Fruits of LLM Pruning: Towards Small Language Models for Efficient Non-Coding Variant Effect Prediction · Table A1, row seed 42, column 'Training Time (Hours)'
Configuration: NT 500M human reference, fine-tuned 5 epochs, unpruned (Hegde et al. 2025)	Protocol: Fine-tuning and evaluation wall time, 5 epochs on the Enformer eQTL dataset (Hegde et al. 2025 Table A1) Dataset: Enformer eQTL causal-variant dataset, Genomics Long-Range Benchmark version (Hegde et al. 2025)	7.48 Runtime (Fine-tuning, 5 epochs, unpruned model) hour · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source NT 500M human reference, 5-epoch fine-tuning time (Hegde et al. 2025 Table A1) gfm-guide-20261014-protocol-hegde-eqtl-finetune-5-epochs-time Aggregation: Not reported Reaping the Fruits of LLM Pruning: Towards Small Language Models for Efficient Non-Coding Variant Effect Prediction · Table A1, row seed 43, column 'Training Time (Hours)'

Source checking is not independent reproduction. Release 2026-10-22-1b7cf027e4f6.

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NT 500M human reference, 5-epoch fine-tuning time (Hegde et al. 2025 Table A1)

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1 source record and release history

Reaping the Fruits of LLM Pruning: Towards Small Language Models for Efficient Non-Coding Variant Effect Prediction · Original source · Genes 16(11):1358, 2025; PMC12652071 full-text XML

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Technical metadata and extraction receipts

Stable ID: gfm-guide-20261014-protocol-hegde-eqtl-finetune-5-epochs-time

areas: dna-genomes
contexts: research
protocol: Fine-tune for 5 epochs on the training split (Hugging Face Trainer), then evaluate binary non-coding variant classification on the evaluation split; record training time in hours and evaluation time in minutes per seed.
version: Genes 2025 Appendix A.3, Table A1
metric: runtime
limitations: Hardware is stated in Methods, not in the table.; Three seeds on one machine; no spread is printed.
source locator: Table A1; Methods paragraph 3

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