Configuration

Geneformer

This single-cell model is evaluated in a study of parameter-efficient adaptation for cell-type identification.

Sources

Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Introduction (paragraph 5); Results/Comparison of proposed PEFT strategies and other finetuning approaches (paragraph 2)

1 evaluation · 1 metric row

How it worksEvaluated procedure (conceptual)

Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings.

Sources

Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Introduction (paragraph 4); Proposed PEFT strategies for scLLMs/Finetuning and evaluation settings (paragraph 1)

At a glance

Model type

Single-cell transformer; this record is the paper-specific evaluated configuration.

Sources

huggingface.co/ctheodoris/Geneformer README.md · README.md model description

Biological inputs

Single-cell gene-expression profiles

Sources

Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · An overview of current scLLMs (paragraph 1); Data and code availability (paragraph 1)

Outputs

Cell-type predictions

Sources

Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Proposed PEFT strategies for scLLMs/Finetuning and evaluation settings (paragraph 2); Results/Comparison of native scLLMs on cell type identification (paragraph 4)

limited source coverage · Automated source review, 2026-09-16. All specifications and missing details

Evaluations and results

Release 2026-09-17-d277315f7d76 · 1 evaluation · 1 metric row. Different protocols are not a single leaderboard.

Evaluation setupMetricEvidence origin

Results grouped by the exact reported evaluation
Metric and finding	Coverage and uncertainty	Evidence
Geneformer: Cell-type identification Configuration: GeneformerTask: Cell-type identificationDataset: M.S. single-cell dataset Native scLLM cell-type identification as reported in Table 2. Independent external evaluation · Evaluation metadata: needs review
0.388 F1-Score Unit: unitless · Direction: unknown	Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported	source checkedParameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Table 2, M.S. / Geneformer row, F1-Score column Source checking is not independent reproduction.

How it works

How the evaluated method works

The study compares ordinary fine-tuning with methods that retain original model parameters while learning additional tensors.

Sources

Underlying method and version boundaries

Geneformer ranks genes by expression scaled against its pretraining corpus, then uses a transformer encoder with a masked-gene objective. V1 and V2 have different corpora, vocabularies, sizes and context limits, so a historical paper name is not replaced with today’s default checkpoint.

Sources

huggingface.co/ctheodoris/Geneformer README.md · README.md; introduction, model description, pretrained-model and usage sections at pinned revision

What was evaluated

The linked evaluation record identifies Geneformer: Cell-type identification. Its dataset, split, adaptation and evidence origin remain attached to the reported results.

Sources

Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-026

Strengths and limitations

Strengths and considerations

Examines the trade-off between adaptation performance and the number of trainable parameters.
Sources
Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Results/Comparison of proposed PEFT strategies and other finetuning approaches (paragraph 3); Results/Comparison of proposed PEFT strategies and other finetuning approaches (paragraph 2)

Limitations and conditions

The base-model label alone does not identify which adaptation tensors were trained; exact PEFT and full-fine-tuning rows must remain separate.
Sources
Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Results/Comparison of proposed PEFT strategies and other finetuning approaches (paragraph 2); Introduction (paragraph 5)

Profile review details

Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction.

Stable record: reported-model-b46ae14b9927ac

Specifications

Inputs, training, access and other details

Explanatory profile: limited source coverage · Automated source review, 2026-09-16. Review applies to the cited claims; unresolved fields are listed below. Numerical results retain their own review status.

Inputs, outputs and configuration
Property	Description and evidence
Model type	Single-cell transformer; this record is the paper-specific evaluated configuration. Sources huggingface.co/ctheodoris/Geneformer README.md · README.md model description
Architecture / procedure	The study compares ordinary fine-tuning with methods that retain original model parameters while learning additional tensors. Sources Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Introduction (paragraph 4); Proposed PEFT strategies for scLLMs/Finetuning and evaluation settings (paragraph 1)
Biological inputs	Single-cell gene-expression profiles Sources Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · An overview of current scLLMs (paragraph 1); Data and code availability (paragraph 1)
Outputs	Cell-type predictions Sources Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Proposed PEFT strategies for scLLMs/Finetuning and evaluation settings (paragraph 2); Results/Comparison of native scLLMs on cell type identification (paragraph 4)
Parameters	An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sources Sources (2) Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification; huggingface.co/ctheodoris/Geneformer README.md · An overview of current scLLMs/Pretrainer:; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision
Known versions / configuration	Geneformer is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sources Sources Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label.
Training data / fitting	Genecorpus-30M pretraining (29.9 million transcriptomes) followed by the paper’s task-specific adaptation comparisons on MS, Zheng68k, NSCLC and COVID-19 datasets. Sources Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · An overview of current scLLMs / Pretrainer; Proposed PEFT strategies / Data preparation
Context limits	A maximum input/context length for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sources Sources (2) Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification; huggingface.co/ctheodoris/Geneformer README.md · An overview of current scLLMs/Pretrainer:; inspected for explicit maximum input length (dataset lengths and family-wide limits are not substituted); README.md at pinned repository revision
Access	Official upstream implementation and usage documentation: https://huggingface.co/ctheodoris/Geneformer/blob/1f7fbae4e469a5f4f1af8c111a529cfe1b3829f5/README.md. This pinned documentation revision is not automatically the evaluated weight revision. Sources huggingface.co/ctheodoris/Geneformer README.md · README.md; installation, model download and usage instructions
Code licence	No explicit code licence was established from the paper’s availability statement and inspected repository-root documentation. · Not reported in inspected sources Sources huggingface.co/ctheodoris/Geneformer README.md · README.md and repository-root licence-file search
Weights licence	Apache 2.0 is declared in the official Geneformer model-card metadata; the exact historical configuration still needs its checkpoint identity. Sources huggingface.co/ctheodoris/Geneformer README.md · README.md front matter, license field; model-version list

Evidence table

Inspect claims, sources and review details

Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.

One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.

22 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-09-17-d277315f7d76
Property and statement	Original source and location	Review and provenance
Diagram caption Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings. Individual claims	Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification Original source ↗ Introduction (paragraph 4); Proposed PEFT strategies for scLLMs/Finetuning and evaluation settings (paragraph 1) Version: preprint archived 2024-01-30 Retrieved: 2026-09-16T10:41:16.530269+00:00	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.diagram.caption` Source artifact SHA-256: `77a4a859010259eadf2187465db6ab385efa4927a5eadb95c1e01991044c283f` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram steps ["Single-cell gene-expression profiles","Geneformer","Cell-type predictions"] Individual claims	Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification Original source ↗ Introduction (paragraph 4); Proposed PEFT strategies for scLLMs/Finetuning and evaluation settings (paragraph 1) Version: preprint archived 2024-01-30 Retrieved: 2026-09-16T10:41:16.530269+00:00	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.diagram.steps` Source artifact SHA-256: `77a4a859010259eadf2187465db6ab385efa4927a5eadb95c1e01991044c283f` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram title Evaluated procedure (conceptual) Individual claims	Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification Original source ↗ Introduction (paragraph 4); Proposed PEFT strategies for scLLMs/Finetuning and evaluation settings (paragraph 1) Version: preprint archived 2024-01-30 Retrieved: 2026-09-16T10:41:16.530269+00:00	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.diagram.title` Source artifact SHA-256: `77a4a859010259eadf2187465db6ab385efa4927a5eadb95c1e01991044c283f` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Model type Single-cell transformer; this record is the paper-specific evaluated configuration. Individual claims	huggingface.co/ctheodoris/Geneformer README.md Original source ↗ README.md model description Version: 1f7fbae4e469a5f4f1af8c111a529cfe1b3829f5 Retrieved: 2026-09-16T19:46:20.640731+00:00	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.0.value` Source artifact SHA-256: `56d6e570b349cbedae9a54634421c94e7af8ea467ce0fdd79193372ae3cbdbd8` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Architecture / procedure The study compares ordinary fine-tuning with methods that retain original model parameters while learning additional tensors. Individual claims	Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification Original source ↗ Introduction (paragraph 4); Proposed PEFT strategies for scLLMs/Finetuning and evaluation settings (paragraph 1) Version: preprint archived 2024-01-30 Retrieved: 2026-09-16T10:41:16.530269+00:00	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.1.value` Source artifact SHA-256: `77a4a859010259eadf2187465db6ab385efa4927a5eadb95c1e01991044c283f` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Weights licence Apache 2.0 is declared in the official Geneformer model-card metadata; the exact historical configuration still needs its checkpoint identity. Individual claims	huggingface.co/ctheodoris/Geneformer README.md Original source ↗ README.md front matter, license field; model-version list Version: 1f7fbae4e469a5f4f1af8c111a529cfe1b3829f5 Retrieved: 2026-09-16T19:46:20.640731+00:00	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.10.value` Source artifact SHA-256: `56d6e570b349cbedae9a54634421c94e7af8ea467ce0fdd79193372ae3cbdbd8` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Biological inputs Single-cell gene-expression profiles Individual claims	Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification Original source ↗ An overview of current scLLMs (paragraph 1); Data and code availability (paragraph 1) Version: preprint archived 2024-01-30 Retrieved: 2026-09-16T10:41:16.530269+00:00	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.2.value` Source artifact SHA-256: `77a4a859010259eadf2187465db6ab385efa4927a5eadb95c1e01991044c283f` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Outputs Cell-type predictions Individual claims	Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification Original source ↗ Proposed PEFT strategies for scLLMs/Finetuning and evaluation settings (paragraph 2); Results/Comparison of native scLLMs on cell type identification (paragraph 4) Version: preprint archived 2024-01-30 Retrieved: 2026-09-16T10:41:16.530269+00:00	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.3.value` Source artifact SHA-256: `77a4a859010259eadf2187465db6ab385efa4927a5eadb95c1e01991044c283f` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims	huggingface.co/ctheodoris/Geneformer README.md Original source ↗ An overview of current scLLMs/Pretrainer:; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 1f7fbae4e469a5f4f1af8c111a529cfe1b3829f5 Retrieved: 2026-09-16T19:46:20.640731+00:00	unreported automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.4.value` Source artifact SHA-256: `56d6e570b349cbedae9a54634421c94e7af8ea467ce0fdd79193372ae3cbdbd8` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims	Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification Original source ↗ An overview of current scLLMs/Pretrainer:; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: preprint archived 2024-01-30 Retrieved: 2026-09-16T10:41:16.530269+00:00	unreported automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.4.value` Source artifact SHA-256: `77a4a859010259eadf2187465db6ab385efa4927a5eadb95c1e01991044c283f` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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Sources and history

Release 2026-09-17-d277315f7d76 · Record review: needs review

2 source records and release history

Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Original source · preprint archived 2024-01-30
huggingface.co/ctheodoris/Geneformer README.md · Original source · 1f7fbae4e469a5f4f1af8c111a529cfe1b3829f5

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Technical metadata and extraction receipts

Stable ID: reported-model-b46ae14b9927ac

areas: cells-tissues
entity level: method
version: Not reported
reported name: Geneformer
historical missing metadata: version: not_reported_in_legacy_extract; checkpoint revision: not_reported_in_legacy_extract; training data: not_reported_in_legacy_extract; licence: not_reported_in_legacy_extract
metadata review scope: historical_missing_metadata preserves the original discovery state. Current descriptive evidence and missingness are recorded in profile.facts; numerical-result review is separate.
legacy kinds: model
entity classification: review date: 2026-09-17; rationale: This source-scoped entry preserves the method/configuration actually named in an evaluation. It is neither a global family identity nor proof of an immutable checkpoint; the linked evaluation retains adaptation, fitting and scoring details.; source ids: single-cell-peft-2024; evidence-reported-base-geneformer-readme-md; source locator: Introduction (paragraph 4); Proposed PEFT strategies for scLLMs/Finetuning and evaluation settings (paragraph 1) | README.md model description | Introduction (paragraph 5); Results/Comparison of proposed PEFT strategies and other finetuning approaches (paragraph 2); ambiguities: Configuration means the source-labelled evaluated identity. It does not establish missing checkpoint hashes, default settings or equivalence to same-named records in other papers.

Related records

model: Geneformer: Cell-type identification

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