Nucleotide Transformer fine-tuning time on a single GPU (Discussion)
Fine tuning time of Nucleotide Transformer
The Discussion states that IA3 fine-tuning, which trains about 0.1% of parameters, allows even the largest models to be fine-tuned in under 15 min on a single GPU. This conflicts with Methods 'Hardware' (one node of eight A100 GPUs, 20 min average for 500M and 50 min for 2.5B models).
Descriptive fact transcribed from the pinned source.
Evidence
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Evidence table
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7 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Claimed field fine_tuning_time Context-only references | Nucleotide Transformer: building and evaluating robust foundation models for human genomics (Nature Methods) Discussion, paragraph 3: 'allowing even our largest models to be fine-tuned in under 15 min on a single GPU.' Version: Nature Methods 22, 287-297 (published online 2024-11-28); PMC11810778 full-text XML | not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Limitations1 values
| Nucleotide Transformer: building and evaluating robust foundation models for human genomics (Nature Methods) Discussion, paragraph 3: 'allowing even our largest models to be fine-tuned in under 15 min on a single GPU.' Version: Nature Methods 22, 287-297 (published online 2024-11-28); PMC11810778 full-text XML | not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Source location Discussion, paragraph 3: 'allowing even our largest models to be fine-tuned in under 15 min on a single GPU.' Context-only references | Nucleotide Transformer: building and evaluating robust foundation models for human genomics (Nature Methods) Discussion, paragraph 3: 'allowing even our largest models to be fine-tuned in under 15 min on a single GPU.' Version: Nature Methods 22, 287-297 (published online 2024-11-28); PMC11810778 full-text XML | not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Claimed value The Discussion states that IA3 fine-tuning, which trains about 0.1% of parameters, allows even the largest models to be fine-tuned in under 15 min on a single GPU. This conflicts with Methods 'Hardware' (one node of eight A100 GPUs, 20 min average for 500M and 50 min for 2.5B models). Context-only references | Nucleotide Transformer: building and evaluating robust foundation models for human genomics (Nature Methods) Discussion, paragraph 3: 'allowing even our largest models to be fine-tuned in under 15 min on a single GPU.' Version: Nature Methods 22, 287-297 (published online 2024-11-28); PMC11810778 full-text XML | not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Description Descriptive fact transcribed from the pinned source. Context-only references | Nucleotide Transformer: building and evaluating robust foundation models for human genomics (Nature Methods) Discussion, paragraph 3: 'allowing even our largest models to be fine-tuned in under 15 min on a single GPU.' Version: Nature Methods 22, 287-297 (published online 2024-11-28); PMC11810778 full-text XML | not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Relationship: subject discovery-model-nucleotide-transformer Context-only references | Nucleotide Transformer: building and evaluating robust foundation models for human genomics (Nature Methods) Discussion, paragraph 3: 'allowing even our largest models to be fine-tuned in under 15 min on a single GPU.' Version: Nature Methods 22, 287-297 (published online 2024-11-28); PMC11810778 full-text XML | not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Name Nucleotide Transformer fine-tuning time on a single GPU (Discussion) Context-only references | Nucleotide Transformer: building and evaluating robust foundation models for human genomics (Nature Methods) Discussion, paragraph 3: 'allowing even our largest models to be fine-tuned in under 15 min on a single GPU.' Version: Nature Methods 22, 287-297 (published online 2024-11-28); PMC11810778 full-text XML | not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
1 source record and release history
- Nucleotide Transformer: building and evaluating robust foundation models for human genomics (Nature Methods) · Original source · Nature Methods 22, 287-297 (published online 2024-11-28); PMC11810778 full-text XML
Technical metadata and extraction receipts
Stable ID: gfm-guide-20261014-claim-nt-fine-tuning-single-gpu
- field
- fine_tuning_time
- value
- The Discussion states that IA3 fine-tuning, which trains about 0.1% of parameters, allows even the largest models to be fine-tuned in under 15 min on a single GPU. This conflicts with Methods 'Hardware' (one node of eight A100 GPUs, 20 min average for 500M and 50 min for 2.5B models).
- source locator
- Discussion, paragraph 3: 'allowing even our largest models to be fine-tuned in under 15 min on a single GPU.'
- review
- method: source-hash-verification; ai-assisted-source-review; method note: Each source sentence the claim rests on asserted verbatim in the reviewer's own text extraction (review/check_gfm_guide.py); the claim text was then read against them.; reviewer: claude; reviewer note: Independent Claude (Opus 5.5) review agent for PR #150; did not extract this batch; no human review claimed; date: 2026-10-14; artifact sha256: 7c3b78a4f38ef053a08e466222e5662dd9711d91c535512d0b35d459d1fc7249; retrieval url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC11810778/fullTextXML; note: Claim matches the source. Review corrected the locator from Discussion paragraph 2 to paragraph 3.
- limitations
- Conflicts with the Methods; see the source concern.
Related records
- subject: Nucleotide Transformer