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NT 500M human reference, fine-tuned 5 epochs, unpruned (Hegde et al. 2025)

InstaDeepAI/nucleotide-transformer-500m-human-ref fine-tuned for binary eQTL variant classification for 5 epochs with all 24 layers.

1 evaluation · 6 results

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1 evaluation · 6 results. Different protocols are not a single leaderboard.

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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)'

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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-config-hegde-nt-500m-human-ref-5-epochs

areas: dna-genomes
contexts: research
method types: foundation_model
reported name: Nucleotide Transformer (original, non-pruned)
checkpoint: InstaDeepAI/nucleotide-transformer-500m-human-ref
hardware: description: 2 × NVIDIA TITAN Xp GPUs (Methods, for the Nucleotide Transformer); Table A1 itself names no hardware
fine tuning: description: LoRA parameter-efficient fine-tuning with the peft package (r=1, lora_alpha=32, lora_dropout=0.1) through the Hugging Face Trainer, 5 epochs (Appendix A.3)
source locator: Appendix A.1; Appendix A.3; Methods paragraph 3
missing metadata: version: reason: unreported; note: Hugging Face repository named without a revision

Related records

configuration of: Nucleotide Transformer
system: NT 500M human reference, 5-epoch fine-tuning time (Hegde et al. 2025 Table A1)

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