Pith. sign in

REVIEW 5 major objections 7 minor 2 cited by

Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification

T0 review · 5 major / 7 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper claims that the gradient response of an LLM's tensor layers to random input noise encodes enough family-specific information to classify fine-tuned derivatives into their architectural family, with 94% accuracy on 58 models.

desk verdict TensorGuard's gradient fingerprint is a genuinely new idea, but the 94% family accuracy is likely carried by the two structural features and the in-sample evaluation does not establish the gradient signal. read the letter →

arxiv 2506.01631 v2 pith:WFKRXXJC submitted 2025-06-02 cs.LG cs.AIcs.SE

Zehao Wu , Yanjie Zhao , Haoyu Wang This is my paper
classification cs.LGcs.AIcs.SE
keywords modelfingerprintinggradientanalysisLLMprovenancefamilyclassificationsimilaritydetectionsafetensorscentroid-initializedK-Meansfine-tuningattribution
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether a fine-tuned or otherwise modified large language model can be traced back to the architectural family it was built from, without access to its training data or an embedded watermark. It proposes TensorGuard, which perturbs a model's inputs with random noise, records the gradient responses of its tensor layers, and condenses them into a 16-dimensional fingerprint per model. The paper reports that, across 58 models (8 base models and 50 derivatives from five families), centroid-initialized K-Means on these fingerprints assigns derivatives to the correct family 94% of the time. If true, this gives model vendors and auditors a technical tool for license compliance and derivative detection that existing watermarking and output-based methods do not provide. The approach also works natively with the safetensors format, which covers most current open-weight models.

What carries the argument

The central object is the 16-dimensional gradient fingerprint. For each sampled tensor layer, the paper injects random noise, computes the gradient $G = x^\top \frac{o}{\|o\|_2}$ of the L2 norm of the layer output with respect to the weight matrix, and records global and per-category statistics: mean, standard deviation, Frobenius norm, and (globally) skewness and kurtosis, alongside total parameter count and number of layers. The perturbation-and-extraction cycle is repeated 30 times with fixed seeds and averaged. PCA reduces the vectors, Euclidean distance supplies pairwise similarity, and centroid-initialized K-Means uses the eight base-model fingerprints as starting centroids for family classification.

What would settle it

Re-run the centroid-initialized K-Means experiment with total_params and num_layers removed from the 16-dimensional fingerprints, or set to identical values for every model; if accuracy does not stay near 94%, the gradient statistics are not doing the classification.

Watch

Extended reading notes

Core claim

TensorGuard's central claim is that the gradient response of an LLM to random input perturbations is a stable, family-specific behavioral signature. Concretely, the paper treats a model as a software artifact, reads it from safetensors shards, merges any adapter weights, injects random noise into input representations, and back-propagates a norm-based loss to obtain per-layer gradients. From these gradients it extracts statistical moments (mean, standard deviation, norm, skewness, kurtosis) plus per-layer-category statistics and two structural descriptors, averaged over 30 perturbation rounds into a 16-dimensional fingerprint. Distance between fingerprints measures model similarity, and centroid-initialized K-Means clustering, seeded with the eight base models, classifies 50 derivatives with 94% accuracy. The paper also reports that random perturbations give far more discriminative per-layer sensitivity than adversarial, frequency-structured, or Gaussian noise, and that centroid initialization beats standard K-Means, GMM, hierarchical clustering, and DBSCAN.

Load-bearing premise

The load-bearing premise is that the gradient statistics, not just total parameter count and layer count, carry the family signal; the paper reports no ablation that removes those two structural features to verify this.

Editorial extensions

If this is right

  • Anyone holding a base model's fingerprint can classify a released derivative into the right architectural family without access to its training data, watermarks, or a matching model format.
  • Parameter-efficient adapter weights can be merged back into the base model before fingerprinting, so lightweight fine-tuning does not erase the family signal.
  • Pairwise Euclidean distance in fingerprint space orders model pairs by similarity, giving a direct similarity check between arbitrary models that the paper reports outperforms the representation-based baseline on safetensors models.
  • Models farther than the distance threshold from every cluster are flagged as out-of-cluster, offering a way to detect novel architectures or heavily modified variants instead of forcing a false family label.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper's claims: the same fingerprint space could be used to detect model merging or interpolation, since a merged model might fall between family centroids; the paper does not test this scenario.
  • Beyond the paper's claims: tracking how fingerprint centroids shift as more derivatives are added could give a quantitative measure of how much a fine-tuning procedure preserves family identity.
  • Beyond the paper's claims: adapting the extraction pipeline to quantized or non-safetensors formats would be a natural test of whether gradient fingerprints survive deployment-level optimizations.
  • Beyond the paper's claims: because the fingerprint captures behavior under perturbation rather than raw weights or outputs, it could in principle compare models of different sizes within the same family, though the paper's evaluation is limited to models at or below 13B parameters.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 7 minor

Summary. The paper proposes TensorGuard, a gradient-based fingerprinting framework for LLM similarity detection and family classification. It extracts a 16-dimensional fingerprint per model from gradient responses to random input perturbations, together with total parameter count and layer count, then uses PCA and centroid-initialized K-Means to classify 50 fine-tuned derivatives of 8 base models into 5 architectural families, reporting 94% accuracy. It also claims that distance-based fingerprints outperform the REEF baseline for pairwise similarity. The evaluation covers three research questions: effectiveness versus baselines, perturbation-strategy choice, and clustering-strategy choice. The central claim is that gradient responses of a model to random input perturbations carry family-specific information sufficient for lineage and license auditing.

Significance. If substantiated, the gradient-based fingerprinting idea would be a valuable contribution to LLM provenance and license-compliance auditing, since it operates without training data or watermarks and supports the safetensors format. The paper has a clear system design, a concrete fingerprint definition, and a non-trivial evaluation dataset of 58 models. However, the evidence as presented does not support the central claim: the reported accuracy can likely be attributed to the two structural features and the in-sample evaluation setup, and the gradient-derived features are never isolated. The paper does not provide machine-checked proofs, a reproducibility artifact link despite referencing one, or falsifiable predictions beyond the in-sample clustering result.

major comments (5)
  1. [III-C and IV-B] The 94% accuracy result does not establish that the gradient-derived features are informative, because the 16-dimensional fingerprint includes total_params and num_layers, and no ablation removes these two structural features. For LoRA, adapter, and full fine-tuned derivatives, these two features are identical to those of the base model by construction, so a centroid-initialized classifier using only these features would separate the eight base models and their derivatives almost perfectly. The paper neither reports a structural-only baseline nor provides PCA loadings or feature-importance analysis, leaving the contribution of the 14 gradient statistics untested.
  2. [III-D.4 and IV-B] The evaluation is in-sample at every stage. PCA is fitted on all 58 models before clustering, the distance threshold of 7 is selected by cross-validation on the same 58 models, and the K-Means centroids are initialized with the fingerprints of the eight base models whose derivatives are the labeled test objects. Consequently the reported 94% figure measures fit to the same data used for training, not predictive accuracy on unseen models, and the term 'unknown model' in Section III-D.4 refers to models within the same set used for PCA and thresholding. A held-out split or a leave-one-family-out evaluation is needed to support the generalization claim.
  3. [III-C and IV-B] The manuscript does not state whether any feature standardization is applied before PCA and Euclidean distance computation. The raw scales differ by roughly nine orders of magnitude (total_params around 1.2e9 versus gradient means around 1e-5 in Figure 2), so if features are not standardized, the two structural features dominate the distances and the gradient statistics are effectively inert. If features are standardized, this must be stated explicitly, and an ablation using only the 14 gradient features (without total_params and num_layers) must be reported to show that they carry independent signal.
  4. [IV-D, Table I] The answer to RQ3 claims that centroid-initialized K-Means achieves 94% accuracy compared to 82% for the best conventional method, but Table I lists Hierarchical Clustering at 78% and GMM at 76%. This numerical inconsistency makes the comparative claim unsupported. Additionally, the comparison conflates the effect of using ground-truth base models as initialized centroids with the effect of the clustering algorithm itself, since the centroids directly encode the family labels of the derivatives.
  5. [IV-C, Answer to RQ2] The claim that random perturbations produce '2.3x higher sensitivity variance' than structured approaches is not accompanied by any variance computation, statistical test, or classification metric. Figure 5 displays layer sensitivity scores, but no aggregate variance is reported, and the connection between layer sensitivity diversity and fingerprint classification accuracy is not established. Without this quantification, RQ2 does not validate the choice of random perturbation as a fingerprint source.
minor comments (7)
  1. [V and VI headings] Section V is headed 'Discusstions' and Section VI 'LLM Similarity Detction'; these should be corrected to 'Discussion' and 'Detection'.
  2. [III-C.3] The text states that 500,000 entries are sampled from G for higher-order statistics, but it does not clarify whether the displayed global_skewness and global_kurtosis are computed on this sampled subset and whether the same random subset is used across models; please specify the sampling procedure and seed handling.
  3. [III-D.4] The distance threshold of 7 is introduced without reporting the scale of the reduced feature space; a validation curve or confusion matrix would make the threshold choice and the out-of-cluster behavior interpretable.
  4. [Figures 4 and 6] The model-name labels in the clustering plots are heavily truncated and unreadable in the submitted PDF, which makes it difficult to verify the three mentioned misclassifications and the clustering boundaries.
  5. [IV-B] REEF is compared only via qualitative heatmap inspection; provide a quantitative similarity metric (e.g., rank correlation or AUC for same-family versus cross-family pairs) to support the claim that TensorGuard outperforms REEF.
  6. [V-B] The limitation discussion mentions Gemma-2B and LLaMA-3.1-3B as having similar fingerprints, but Gemma-2B is not part of the evaluation dataset; clarify whether this comes from additional experiments and report the corresponding distances.
  7. [III-C] The text references a 'replication artifact' for fixed random seeds and implementation details, but no artifact link or repository is provided in the paper; please include one for reproducibility.

Circularity Check

1 steps flagged · score 4.0 of 10

The 94% family-classification figure is partly an in-sample fit: PCA and cluster centroids are computed on the same 58 models that are then classified, so the reported accuracy is not an independent prediction for unseen derivatives.

  1. fitted input called prediction [Section III-D.3 (centroid-initialized clustering); evaluation in Section IV-B/Table I]
    "Rather than employing traditional randomly initialized K-Means clustering, we modify the K-Means algorithm to initialize centroids with established base model fingerprints... centroids are allowed to adjust through iterative updates while maintaining their connection to known architectural families. After convergence, each cluster represents a model family with its centroid reflecting the mean fingerprint characteristics of all member models within that family."

    The 94% family-classification accuracy is computed on the same 58-model dataset that is clustered, after PCA is fit on the full set. Because the final centroid for a family is the mean fingerprint of all member models in that cluster, each derivative's distance to its own family centroid is partly determined by its own fingerprint; this is not a held-out or out-of-sample prediction. The abstract's promise of "family classification of unknown models" is therefore stronger than what was measured: the reported number is an in-sample cluster fit rather than an independent classification of previously unseen derivatives.

full rationale

The paper's gradient computation in Eq. (5) is a standard chain-rule identity and is not circular. Family labels come from external HuggingFace model-card metadata, and the paper cites no self-authored uniqueness theorem or prior own work as load-bearing support. The main circularity-adjacent concern is that the headline 94% classification accuracy is reported on the same dataset used to fit the PCA projection and to adjust the cluster centroids: after convergence each centroid is the mean fingerprint of the family's members, so each derivative contributes to the centroid against which it is scored. That makes the number an in-sample clustering fit rather than a measurement of classification performance on "unknown models" as claimed. A second validity threat, the confound between the two structural features (total_params and num_layers, which derivatives inherit unchanged from their base models) and the 14 gradient features, is a missing-ablation problem rather than a definitional circularity; it does not by itself establish that the gradient features are uninformative. Accordingly, the circularity score is moderate rather than severe.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The method introduces no new physical entities. The load-bearing free parameters are the distance threshold, PCA dimensionality, and various noise magnitudes, none of which are tied to theory. The core assumption that gradient statistics are family-discriminative is exactly what the evaluation fails to isolate.

free parameters (5)
  • Distance threshold (out-of-cluster) = 7
    Set empirically through cross-validation in Section III-D4; directly determines whether an unknown model is assigned to a family or marked out-of-cluster.
  • PCA target dimensionality = 2
    Chosen to preserve 'majority of variance' and allow visualization; affects clustering and the reported accuracy.
  • Noise magnitudes (epsilon for FGSM, variance for Gaussian and frequency noise)
    Not reported in the paper; the strength of the injected perturbation determines the gradient statistics that form the fingerprint, so these values are load-bearing but unspecified.
  • Number of perturbation iterations = 30
    Fixed at 30 and averaged to define the fingerprint; arbitrary choice that affects stability and cost.
  • Fixed random seed = unspecified
    Used to guarantee the same noise sequences across models; the particular seed value is not given.
assumptions (4)
  • domain assumption Gradient statistics of a linear layer under random input noise are stable and family-discriminative.
    The entire method assumes these features capture architectural lineage; the paper offers empirical evidence but no derivation or test against trivial features.
  • domain assumption Fine-tuned derivatives preserve the base model's tensor names, shapes, and layer organization.
    Preprocessing aligns models by tensor name and category; if fine-tuning significantly changed the architecture, the fingerprint would be incomparable. Stated in Section III-C.
  • domain assumption The L2-norm loss gradient with respect to each weight matrix is a meaningful behavioral signature.
    Section III-C3 defines the gradient as the fingerprint foundation; this choice is not compared to alternatives such as Hessian or activation statistics.
  • ad hoc to paper PCA fitted on the full set of 58 models is an appropriate representation for classifying those same models.
    The PCA is learned on the test distribution, and the paper does not assess generalization to unseen models, making this a fitted component of the evaluation rather than a validated analysis.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification." pith.science (2026). https://pith.science/paper/WFKRXXJC

@misc{pith2026250601631,
  author       = {Pith},
  title        = {Pith review of: Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WFKRXXJC}},
  note         = {Machine review of arXiv:2506.01631}
}
read the original abstract

As Large Language Models (LLMs) become integral software components in modern applications, unauthorized model derivations through fine-tuning, merging, and redistribution have emerged as critical software engineering challenges. Unlike traditional software where clone detection and license compliance are well-established, the LLM ecosystem lacks effective mechanisms to detect model lineage and enforce licensing agreements. This gap is particularly problematic when open-source model creators, such as Meta's LLaMA, require derivative works to maintain naming conventions for attribution, yet no technical means exist to verify compliance. To fill this gap, treating LLMs as software artifacts requiring provenance tracking, we present TensorGuard, a gradient-based fingerprinting framework for LLM similarity detection and family classification. Our approach extracts model-intrinsic behavioral signatures by analyzing gradient responses to random input perturbations across tensor layers, operating independently of training data, watermarks, or specific model formats. TensorGuard supports the widely-adopted safetensors format and constructs high-dimensional fingerprints through statistical analysis of gradient features. These fingerprints enable two complementary capabilities: direct pairwise similarity assessment between arbitrary models through distance computation, and systematic family classification of unknown models via the K-Means clustering algorithm with domain-informed centroid initialization using known base models. Experimental evaluation on 58 models comprising 8 base models and 50 derivatives across five model families (Llama, Qwen, Gemma, Phi, Mistral) demonstrates 94% classification accuracy under our centroid-initialized K-Means clustering.

Figures

Figures reproduced from arXiv: 2506.01631 by the authors.

Figure 1
Figure 1. The system structure of TENSORGUARD. automates the merging procedure across multiple model di￾rectories. The script incorporates safeguards to skip previously merged models and ensures that all outputs conform to a uni￾fied format specification. This consolidation process guaran￾tees compatibility with the gradient-based analysis framework described in the subsequent section. C. Tensor Analysis and Fingerprint Extra… view at source ↗
Figure 2
Figure 2. Example of the extracted features from Llama3.2-1B. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. CKA similarity heatmaps generated by REEF, com [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Centroid-initialized K-Means clustering results for five [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Sensitivity analysis of Llama-3.2-1B under five structured perturbation types and one random noise baseline. Attention￾related layers (orange) tend to show lower sensitivity, while down_proj consistently exhibits higher vulnerability. Llama3.2-3B, authormist/authormist…
Figure 6
Figure 6. Figure 6: Visualization of four clustering methods using PCA-projected fingerprint vectors. Each method reveals different boundary [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. KBF: Knowledge Boundary as Fingerprint for Language Model and Black-Box API Auditing

    cs.CR 2026-05 unverdicted novelty 7.0 of 10

    KBF uses stable numerical recall near the knowledge boundary to fingerprint and audit black-box LLM APIs, successfully detecting all tested substitutions and some real-world inconsistencies across production endpoints.

  2. modelDNA: Calibrated Lineage Verification and Merge Decomposition from Sampled Weight Fingerprints

    cs.LG 2026-07 conditional novelty 6.5 of 10

    Sampled weight fingerprints recover LLM parentage with AUROC 1.0 and zero false positives, and recover published mergekit mixture weights without full downloads.

Reference graph

Works this paper leans on

49 extracted references · 27 canonical work pages · cited by 2 Pith papers

  1. [1]

    Hugging face-models,

    Hugging Face, “Hugging face-models,” 2025. [Online]. Available: https://huggingface.co/models

    work page 2025

  2. [2]

    Code clone detection—a systematic review,

    G. Shobha, A. Rana, V . Kansal, and S. Tanwar, “Code clone detection—a systematic review,”Emerging Technologies in Data Mining and Infor- mation Security: Proceedings of IEMIS 2020, Volume 2, pp. 645–655, 2021

    work page 2020

  3. [3]

    Understanding the role of licenses and evolution in open architecture software ecosystems,

    W. Scacchi and T. A. Alspaugh, “Understanding the role of licenses and evolution in open architecture software ecosystems,”Journal of Systems and Software, vol. 85, no. 7, pp. 1479–1494, 2012

    work page 2012

  4. [4]

    Meta llama 3 license

    Meta Platforms, “Meta llama 3 license.” 2025. [Online]. Available: https://www.llama.com/llama3/license/

    work page 2025

  5. [5]

    Gemma 3 terms of use,

    Gemma Project, “Gemma 3 terms of use,” 2025. [Online]. Available: https://gemma3.org/terms

    work page 2025

  6. [6]

    A survey of static analysis methods for identifying security vulnerabilities in software systems,

    M. Pistoia, S. Chandra, S. J. Fink, and E. Yahav, “A survey of static analysis methods for identifying security vulnerabilities in software systems,”IBM systems journal, vol. 46, no. 2, pp. 265–288, 2007

    work page 2007

  7. [7]

    The explosion of small language models (slms) and license confusion,

    Gretel.ai, “The explosion of small language models (slms) and license confusion,” 2024. [Online]. Available: https://gretel.ai/blog/ the-explosion-of-slms-and-license-confusion

    work page 2024

  8. [8]

    Instructional fingerprinting of large language models,

    J. Xu, F. Wang, M. D. Ma, P. W. Koh, C. Xiao, and M. Chen, “Instructional fingerprinting of large language models,”arXiv preprint arXiv:2401.12255, 2024

    arXiv 2024

Show all 49 references
  1. [9]

    Hey, that’s my model! introduc- ing chain & hash, an llm fingerprinting technique,

    M. Russinovich and A. Salem, “Hey, that’s my model! introduc- ing chain & hash, an llm fingerprinting technique,”arXiv preprint arXiv:2407.10887, 2024

    2024 arXiv

  2. [10]

    A fingerprint for large language models,

    Z. Yang and H. Wu, “A fingerprint for large language models,”arXiv preprint arXiv:2407.01235, 2024

    2024

  3. [11]

    Your large language models are leaving fingerprints,

    H. McGovern, R. Stureborg, Y . Suhara, and D. Alikaniotis, “Your large language models are leaving fingerprints,”arXiv preprint arXiv:2405.14057, 2024

    2024 arXiv

  4. [12]

    Reef: Representation encoding fingerprints for large language models,

    J. Zhang, D. Liu, C. Qian, L. Zhang, Y . Liu, Y . Qiao, and J. Shao, “Reef: Representation encoding fingerprints for large language models,”arXiv preprint arXiv:2410.14273, 2024

    2024 arXiv

  5. [13]

    Huref: Human-readable fingerprint for large language models,

    B. Zeng, L. Wang, Y . Hu, Y . Xu, C. Zhou, X. Wang, Y . Yu, and Z. Lin, “Huref: Human-readable fingerprint for large language models,”Ad- vances in Neural Information Processing Systems, vol. 37, pp. 126 332– 126 362, 2024

    2024

  6. [14]

    On the origin of llamas: Model tree heritage recovery,

    E. Horwitz, A. Shul, and Y . Hoshen, “On the origin of llamas: Model tree heritage recovery,”arXiv preprint arXiv:2405.18432, 2024

    2024 arXiv

  7. [15]

    Models-hugging face,

    H. Face, “Models-hugging face,” 2025. [Online]. Available: https: //huggingface.co/models?library=safetensors

    2025

  8. [16]

    Pain pickle: Bypassing python restricted unpickler for automatic exploit generation,

    N.-J. Huang, C.-J. Huang, and S.-K. Huang, “Pain pickle: Bypassing python restricted unpickler for automatic exploit generation,” in2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS). IEEE, 2022, pp. 1079–1090

    2022

  9. [17]

    Safetensors documentation,

    H. Face, “Safetensors documentation,” 2024. [Online]. Available: https://huggingface.co/docs/safetensors/en/index

    2024

  10. [18]

    Fine-tuning pretrained language models: Weight initializa- tions, data orders, and early stopping,

    J. Dodge, G. Ilharco, R. Schwartz, A. Farhadi, H. Hajishirzi, and N. Smith, “Fine-tuning pretrained language models: Weight initializa- tions, data orders, and early stopping,”arXiv preprint arXiv:2002.06305, 2020

    2002 arXiv

  11. [19]

    Parameter-efficient fine-tuning methods for pretrained language models: A critical review and assessment,

    L. Xu, H. Xie, S.-Z. J. Qin, X. Tao, and F. L. Wang, “Parameter-efficient fine-tuning methods for pretrained language models: A critical review and assessment,”arXiv preprint arXiv:2312.12148, 2023

    2023 arXiv

  12. [20]

    The power of scale for parameter-efficient prompt tuning,

    B. Lester, R. Al-Rfou, and N. Constant, “The power of scale for parameter-efficient prompt tuning,”arXiv preprint arXiv:2104.08691, 2021

    2021 arXiv

  13. [21]

    Fine-tuning language models from human preferences,

    D. M. Ziegler, N. Stiennon, J. Wu, T. B. Brown, A. Radford, D. Amodei, P. Christiano, and G. Irving, “Fine-tuning language models from human preferences,”arXiv preprint arXiv:1909.08593, 2019

    1909 arXiv

  14. [22]

    Bert: Pre-training of deep bidirectional transformers for language understanding,

    J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” inPro- ceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technolog...

    2019

  15. [23]

    Parameter-efficient transfer learning for nlp,

    N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” inInternational conference on machine learning. PMLR, 2019, pp. 2790–2799

    2019

  16. [24]

    Prefix-tuning: Optimizing continuous prompts for generation,

    X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,”arXiv preprint arXiv:2101.00190, 2021

    2021 arXiv

  17. [25]

    Lora: Low-rank adaptation of large language models

    E. J. Hu, Y . Shen, P. Wallis, Z. Allen-Zhu, Y . Li, S. Wang, L. Wang, W. Chenet al., “Lora: Low-rank adaptation of large language models.” ICLR, vol. 1, no. 2, p. 3, 2022

    2022

  18. [26]

    Intrinsic dimensionality explains the effectiveness of language model fine-tuning,

    A. Aghajanyan, L. Zettlemoyer, and S. Gupta, “Intrinsic dimensionality explains the effectiveness of language model fine-tuning,”arXiv preprint arXiv:2012.13255, 2020

    2012 arXiv

  19. [27]

    Visual instruction tuning,

    H. Liu, C. Li, Q. Wu, and Y . J. Lee, “Visual instruction tuning,” Advances in neural information processing systems, vol. 36, pp. 34 892– 34 916, 2023

    2023

  20. [28]

    Rlaif vs. rlhf: Scaling reinforce- ment learning from human feedback with ai feedback,

    H. Lee, S. Phatale, H. Mansoor, T. Mesnard, J. Ferret, K. Lu, C. Bishop, E. Hall, V . Carbune, A. Rastogiet al., “Rlaif vs. rlhf: Scaling reinforce- ment learning from human feedback with ai feedback,”arXiv preprint arXiv:2309.00267, 2023

    2023 arXiv

  21. [29]

    The fine-tuning landscape in 2025: A comprehensive analysis,

    Pradeep Das, “The fine-tuning landscape in 2025: A comprehensive analysis,” 2025. [On- line]. Available: https://medium.com/%40pradeepdas/ the-fine-tuning-landscape-in-2025-a-comprehensive-analysis-d650d24bed97

    2025

  22. [30]

    A systematic literature review of parameter-efficient fine-tuning for large code models,

    M. Z. Haque, S. Afrin, and A. Mastropaolo, “A systematic literature review of parameter-efficient fine-tuning for large code models,”arXiv preprint arXiv:2504.21569, 2025

    2025 arXiv

  23. [31]

    Ia3: Parameter-efficient fine-tuning with ia3,

    H. Face, “Ia3: Parameter-efficient fine-tuning with ia3,” https:// huggingface.co/docs/peft/conceptual guides/ia3, 2023

    2023

  24. [32]

    Qwen2.5-7b-instruct on hugging face,

    Qwen, “Qwen2.5-7b-instruct on hugging face,” https: //huggingface.co/Qwen/Qwen2.5-7B-Instruct/tree/main?show file info=model-00001-of-00004.safetensors, 2024

    2024

  25. [33]

    Phi-4 on hugging face,

    Microsoft, “Phi-4 on hugging face,” https://huggingface.co/microsoft/ phi-4/tree/main?show file info=model-00001-of-00006.safetensors, 2024

    2024

  26. [34]

    Explaining and harnessing adversarial examples,

    I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,”arXiv preprint arXiv:1412.6572, 2014

    2014 arXiv

  27. [35]

    Principal components analysis (pca),

    A. Ma ´ckiewicz and W. Ratajczak, “Principal components analysis (pca),” Computers & Geosciences, vol. 19, no. 3, pp. 303–342, 1993

    1993

  28. [36]

    Llama 3.1-8b on hugging face,

    Meta AI, “Llama 3.1-8b on hugging face,” 2024. [Online]. Available: https://huggingface.co/meta-llama/Llama-3.1-8B

    2024

  29. [37]

    Llama 3.2-1b on hugging face,

    ——, “Llama 3.2-1b on hugging face,” 2024. [Online]. Available: https://huggingface.co/meta-llama/Llama-3.2-1B

    2024

  30. [38]

    Llama 3.2-3b on hugging face,

    ——, “Llama 3.2-3b on hugging face,” 2024. [Online]. Available: https://huggingface.co/meta-llama/Llama-3.2-3B

    2024

  31. [39]

    Qwen2.5-3b on hugging face,

    Qwen Team, “Qwen2.5-3b on hugging face,” 2024. [Online]. Available: https://huggingface.co/Qwen/Qwen2.5-3B

    2024

  32. [40]

    Qwen2.5-7b on hugging face,

    ——, “Qwen2.5-7b on hugging face,” 2024. [Online]. Available: https://huggingface.co/Qwen/Qwen2.5-7B

    2024

  33. [41]

    Phi-4 on hugging face,

    Microsoft, “Phi-4 on hugging face,” 2024. [Online]. Available: https://huggingface.co/microsoft/phi-4

    2024

  34. [42]

    Gemma 3 4b it on hugging face,

    Google DeepMind, “Gemma 3 4b it on hugging face,” 2024. [Online]. Available: https://huggingface.co/google/gemma-3-4b-it

    2024

  35. [43]

    Mistral-7b-instruct-v0.1 on hugging face,

    Mistral AI, “Mistral-7b-instruct-v0.1 on hugging face,” 2024. [Online]. Available: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1

    2024

  36. [44]

    Extending llama-3’s context ten-fold overnight,

    P. Zhang, N. Shao, Z. Liu, S. Xiao, H. Qian, Q. Ye, and Z. Dou, “Extending llama-3’s context ten-fold overnight,”arXiv preprint arXiv:2404.19553, 2024

    2024 arXiv

  37. [45]

    Fine-tuning qwen 2.5 3b for realistic movie dialogue generation,

    K. Gupta, “Fine-tuning qwen 2.5 3b for realistic movie dialogue generation,”arXiv preprint arXiv:2502.16274, 2025

    2025 arXiv

  38. [46]

    Fine-tuning gemma-7b for enhanced sentiment analysis of financial news headlines,

    K. Mo, W. Liu, X. Xu, C. Yu, Y . Zou, and F. Xia, “Fine-tuning gemma-7b for enhanced sentiment analysis of financial news headlines,” in2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI). IEEE, 2024, pp. 130–135

    2024

  39. [47]

    Fine-tuning large language models for adaptive machine translation,

    Y . Moslem, R. Haque, and A. Way, “Fine-tuning large language models for adaptive machine translation,”arXiv preprint arXiv:2312.12740, 2023

    2023 arXiv

  40. [48]

    Gemma 2 2b it on hugging face,

    Google DeepMind, “Gemma 2 2b it on hugging face,” 2024. [Online]. Available: https://huggingface.co/google/gemma-2-2b-it

    2024

  41. [49]

    Piracy resistant watermarks for deep neural networks,

    H. Li, E. Wenger, S. Shan, B. Y . Zhao, and H. Zheng, “Piracy resistant watermarks for deep neural networks,”arXiv preprint arXiv:1910.01226, 2019

    1910 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.