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get_layers

Syntologyentry name in harvested coderead from the graph 2026-09-24

get_layers appears in the code Syntology harvested for 20 papers, as 16 distinct code bodies found in 20 places (a place is one code body under one paper). At least one of them ran in 3 of the papers; 0 of the code bodies carry a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named get_layers do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 3 of the 16 distinct code bodies named get_layers; 13 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
2ran · our draft was wrong
0ran · fixture could not drive it
1ran
13unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 3 of the 20 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

20 papers shown of 20, newest first; 20 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive, and the graph's for 5 papers added by Syntology; 3 papers have no page here and are shown by arXiv id only. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's code_sha256, Syntology's identity for that exact code: an agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

PaperDateFileStatus SyntologyLicence
Ghosted Layers: Unconstrained Activation Alignment for Recovering Layer-Pruned LLMs added by Syntology 2026-05 (from id) chenxinrui-tsinghua/LinearPatch/rotate_utils/model_utils.py eb59d0a73734e449 unverified MIT (permissive)
Rethinking Residual Errors in Compensation-based LLM Quantization added by Syntology 2026-04 (from id) list0830/ResComp/fake_quant/model_utils.py eb59d0a73734e449 unverified no licence file found · pointer only
Doc-to-LoRA: Learning to Instantly Internalize Contexts added by Syntology 2026-02 (from id) SakanaAI/doc-to-lora/src/ctx_to_lora/modeling/text_to_lora.py eb1fa1a3b994be5d ran · our draft was wrong MIT (permissive)
D 2 Quant: Accurate Low-bit Post-Training Weight Quantization for LLMs added by Syntology 2026-02 (from id) XIANGLONGYAN/D2Quant/d2quant/model_utils.py 5cfb768c5400311b unverified Apache-2.0 (permissive)
DartQuant: Efficient Rotational Distribution Calibration for LLM Quantization added by Syntology 2025-11 (from id) CAS-CLab/DartQuant/NPU_DartQuant/fake_quant/model_utils.py eb59d0a73734e449 unverified no licence file found · pointer only
OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting 23 Jan 2025 brotherhappy/ostquant/utils/model_utils.py 402d788d51ddf8ca unverified Apache-2.0 (permissive)
Pushing the Limits of Large Language Model Quantization via the Linearity Theorem 26 Nov 2024 goodevening13/aquakv/aquakv/modelutils.py 6af39b9342e5f24f unverified Apache-2.0 (permissive)
QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs 30 Mar 2024 spcl/quarot/fake_quant/model_utils.py eb59d0a73734e449 unverified Apache-2.0 (permissive)
Massive Activations in Large Language Models 27 Feb 2024 bluorion-com/refine_massive_activations/utils/model.py 16dc8236499dd526 unverified MIT (permissive)
1-Lipschitz Layers Compared: Memory, Speed, and Certifiable Robustness 28 Nov 2023 berndprach/1lipschitzlayerscompared/models/simplified_conv_net.py c7c9cb838427bef2 unverified MIT (permissive)
Accurate Retraining-free Pruning for Pretrained Encoder-based Language Models 7 Aug 2023 snudm-starlab/k-prune/src/utils/arch.py e710f0bc27cebd18 ran no licence file found · pointer only
Outliers Dimensions that Disrupt Transformers Are Driven by Frequency 23 May 2022 gpucce/outliersvsfreq/outliersvsfreq/parameter_access.py 2349f1f44cdb663a unverified Apache-2.0 (permissive)
Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation 11 Jan 2021 eth-sri/fnf/generative/flow.py 2f44b49a20c9cd67 ran · our draft was wrong Apache-2.0 (permissive)
UnMask: Adversarial Detection and Defense Through Robust Feature Alignment 21 Feb 2020 safreita1/unmask/utils.py 068d3d6699b81f75 unverified MIT (permissive)
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations 4 Apr 2019 fredhohman/summit-notebooks/influence.py 7c545c01301a1eed unverified MIT (permissive)
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks 26 Nov 2018 freedombenLiu/gandissect/netdissect/server.py a7ff4317d03c4c34 unverified MIT (permissive)
A Neural Algorithm of Artistic Style 26 Aug 2015 arvind1998/Neural-Style-Transfer/model.py 11905b0b5a3383e5 unverified MIT (permissive)
arXiv:aaai_26027 UCAS-LCH/Twin-Rep/model_utils.py 1898431b4546fe6c unverified MIT (permissive)
arXiv:Prach_1-Lipschitz_Layers_Compared_Memory_Speed_and_Certifiable_Robustness_CVPR_2024_paper berndprach/1LipschitzLayersCompared/models/simplified_conv_net.py c7c9cb838427bef2 unverified MIT (permissive)
arXiv:2025.acl-long.175 jinghan1he/VHR/utils.py 31736c6b695804ef unverified Apache-2.0 (permissive)

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the harvest. "Pointer only" means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

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