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1 evaluation · 1 result. Different protocols are not a single leaderboard.
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: NT 2.5B, 4-bit weights for embedding inference (Saadat and Fellay 2026) | Protocol: Peak memory under 8-bit and 4-bit quantization relative to BF16 (Saadat and Fellay 2026 Figure 5) Dataset: Synthetic uniform random DNA sequences, seed 42 (Saadat and Fellay 2026) | 5.6 Peak memory (Embedding inference with 4-bit weights; the paper calls it parameter memory in Section 2.4 and peak memory in Figure 5) gigabyte · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceNT 2.5B 4-bit inference memory saadat-fellay-20261015-protocol-quantization-peak-memory Aggregation: Not reported Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) · Section 3.5 'Memory' paragraph (page 7): 'For NT 2.5B, memory decreases from 6.7 GB (BF16) to 5.6 GB (4-bit).' |
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Peak memory under 8-bit and 4-bit quantization relative to BF16 (Saadat and Fellay 2026 Figure 5)
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19 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Comparison: adaptation None (pretrained weights) Context-only references | Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Section 3.5 'Memory' paragraph (page 7); Figure 5 right panel Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: aggregation Not reported Context-only references | Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Section 3.5 'Memory' paragraph (page 7); Figure 5 right panel Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) | missing or unspecified No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: budget Not reported Context-only references | Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Section 3.5 'Memory' paragraph (page 7); Figure 5 right panel Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) | missing or unspecified No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: dataset version Not reported Context-only references | Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Section 3.5 'Memory' paragraph (page 7); Figure 5 right panel Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) | missing or unspecified No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: inputs DNA sequence Context-only references | Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Section 3.5 'Memory' paragraph (page 7); Figure 5 right panel Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: metric implementation Not reported Context-only references | Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Section 3.5 'Memory' paragraph (page 7); Figure 5 right panel Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) | missing or unspecified No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: population Not reported Context-only references | Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Section 3.5 'Memory' paragraph (page 7); Figure 5 right panel Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) | missing or unspecified No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: protocol id saadat-fellay-20261015-protocol-quantization-peak-memory Context-only references | Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Section 3.5 'Memory' paragraph (page 7); Figure 5 right panel Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: split Not applicable Context-only references | Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Section 3.5 'Memory' paragraph (page 7); Figure 5 right panel Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Inference hardware NVIDIA H100 (section 2.4); 'H100 94GB GPUs' (section 4) Context-only references | Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Section 3.5 'Memory' paragraph (page 7); Figure 5 right panel Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
Stable ID: saadat-fellay-20261015-eval-nt-2-5b-quantization-memory-4bit