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get_ptb

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

get_ptb appears in the code Syntology harvested for 43 papers, as 26 distinct code bodies found in 43 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_ptb 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 26 distinct code bodies named get_ptb; 23 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
0ran · our draft was wrong
0ran · fixture could not drive it
3ran
23unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 14 of the 43 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

43 papers shown of 43, newest first; 43 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 3 papers added by Syntology; 7 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
ProjQ: Project-and-Quantize for Adapter-Aware LLM Compression added by Syntology 2026-06 (from id) yy9301/ProjQ/datautils.py d313d28375a69ab9 ran no licence file found · pointer only
Garbage Attention in Large Language Models: <BOS> Sink Heads and Sink-aware Pruning added by Syntology 2026-01 (from id) CASIA-LMC-Lab/FLAP/lib/prune.py 1d035152616b1d19 unverified Apache-2.0 (permissive)
PT$^2$-LLM: Post-Training Ternarization for Large Language Models added by Syntology 2025-10 (from id) XIANGLONGYAN/PT2-LLM/pt2_llm/data.py 1b0736b420b42de6 unverified Apache-2.0 (permissive)
arXiv:2506.09351 2025-06 (from id) yuchenblah/DIVE/lib/evaldata.py 6086a5ef9f4b6277 unverified Apache-2.0 (permissive)
arXiv:2506.03781 2025-06 (from id) OpenGVLab/OmniQuant/datautils.py bd4f416e271c2fb5 unverified MIT (permissive)
DLP: Dynamic Layerwise Pruning in Large Language Models 27 May 2025 ironartisan/dlp/lib/prune.py 6286af97d1bc3835 unverified Apache-2.0 (permissive)
An Empirical Study of Qwen3 Quantization 4 May 2025 efficient-ml/qwen3-quantization/BiLLM/datautils.py a7cbf4fc49c71e03 unverified Apache-2.0 (permissive)
Pushing the Limits of Large Language Model Quantization via the Linearity Theorem 26 Nov 2024 goodevening13/aquakv/aquakv/datautils.py 330e351424a2b37b unverified Apache-2.0 (permissive)
MC-MoE: Mixture Compressor for Mixture-of-Experts LLMs Gains More 8 Oct 2024 Aaronhuang-778/MC-MoE/datautils.py a7cbf4fc49c71e03 unverified no licence file found · pointer only
Two Sparse Matrices are Better than One: Sparsifying Neural Networks with Double Sparse Factorization 27 Sep 2024 usamec/double_sparse/datautils.py a7cbf4fc49c71e03 unverified Apache-2.0 (permissive)
LeanQuant: Accurate Large Language Model Quantization with Loss-Error-Aware Grid 14 Jul 2024 LeanModels/LeanQuant/datautils.py f810b1ddd55b5375 ran no licence file found · pointer only
Composable Interventions for Language Models 9 Jul 2024 hartvigsen-group/composable-interventions/sparsellm/lib/datautils.py 3a6ac4325dad94a4 unverified no licence file found · pointer only
Rethinking Pruning Large Language Models: Benefits and Pitfalls of Reconstruction Error Minimization 21 Jun 2024 log-postech/rethinking-llm-pruning/lib/data.py 3b99c66b488c6dc0 unverified no licence file found · pointer only
Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox 15 Jun 2024 tsingmaoai/mi-optimize/mi_optimize/datasets/data_loader.py f6e95ffd6622a1b4 unverified licence not identified · pointer only
ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization 10 Jun 2024 gatech-eic/shiftaddllm/datautils.py 7f7c3deb52669856 unverified Apache-2.0 (permissive)
DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs 3 Jun 2024 Hsu1023/DuQuant/datautils.py bd4f416e271c2fb5 unverified MIT (permissive)
MagR: Weight Magnitude Reduction for Enhancing Post-Training Quantization 2 Jun 2024 AozhongZhang/MagR/datautils.py 3a6ac4325dad94a4 unverified MIT (permissive)
Sparse Expansion and Neuronal Disentanglement 24 May 2024 shavit-lab/sparse-expansion/utils/datautils.py a7cbf4fc49c71e03 unverified MIT (permissive)
SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models 23 May 2024 Aaronhuang-778/SliM-LLM/slim-llm-plus/datautils.py bd4f416e271c2fb5 unverified no licence file found · pointer only
An empirical study of LLaMA3 quantization: from LLMs to MLLMs 22 Apr 2024 macaronlin/llama3-quantization/datautils.py bd4f416e271c2fb5 unverified no licence file found · pointer only
AffineQuant: Affine Transformation Quantization for Large Language Models 19 Mar 2024 bytedance/affinequant/datautils.py bd4f416e271c2fb5 unverified Apache-2.0 (permissive)
FrameQuant: Flexible Low-Bit Quantization for Transformers 10 Mar 2024 vsingh-group/framequant/datautils.py 3265f727a396f15d unverified no licence file found · pointer only
SparseLLM: Towards Global Pruning for Pre-trained Language Models 28 Feb 2024 BaiTheBest/SparseLLM/datautils.py a7cbf4fc49c71e03 unverified Apache-2.0 (permissive)
BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation 18 Feb 2024 linkanonymous/besa/utils/data.py 3c22f52a4d22d5d2 unverified no licence file found · pointer only
OneBit: Towards Extremely Low-bit Large Language Models 17 Feb 2024 xuyuzhuang11/OneBit/evaluation/lm_eval/datautils.py 90dfde049522b716 unverified MIT (permissive)
NutePrune: Efficient Progressive Pruning with Numerous Teachers for Large Language Models 15 Feb 2024 lucius-lsr/nuteprune/eval_ppl.py 6ada2617ee1d6042 unverified no licence file found · pointer only
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs 6 Feb 2024 aaronhuang-778/billm/datautils.py a7cbf4fc49c71e03 unverified MIT (permissive)
Fluctuation-based Adaptive Structured Pruning for Large Language Models 19 Dec 2023 casia-iva-lab/flap/lib/data.py 1d035152616b1d19 unverified Apache-2.0 (permissive)
Atom: Low-bit Quantization for Efficient and Accurate LLM Serving 29 Oct 2023 efeslab/atom/model/datautils.py d71aa8d60119d553 unverified no licence file found · pointer only
One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language Models 14 Oct 2023 talkking/MixGPT/datautils.py 7d205a0e647139ae unverified no licence file found · pointer only
QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language Models 13 Oct 2023 ist-daslab/quik/experiments/datautils.py 614c29352fee019e ran Apache-2.0 (permissive)
Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs 13 Oct 2023 zyxxmu/dsnot/lib/prune.py 3b99c66b488c6dc0 unverified no licence file found · pointer only
QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models 12 Oct 2023 modeltc/qllm/datautils.py 1b68bae7cd1dd862 unverified Apache-2.0 (permissive)
PB-LLM: Partially Binarized Large Language Models 29 Sep 2023 hahnyuan/binaryllm/gptq_pb/datautils.py a7cbf4fc49c71e03 unverified MIT (permissive)
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models 25 Aug 2023 opengvlab/omniquant/datautils.py bd4f416e271c2fb5 unverified MIT (permissive)
ChatHome: Development and Evaluation of a Domain-Specific Language Model for Home Renovation 28 Jul 2023 lianjiatech/belle/models/gptq/datautils.py c1c9acf2a2b2ad95 unverified Apache-2.0 (permissive)
Llama 2: Open Foundation and Fine-Tuned Chat Models 18 Jul 2023 squeezeailab/squeezellm/squeezellm/datautils.py b1e98f962f5ae82d unverified MIT (permissive)
RPTQ: Reorder-based Post-training Quantization for Large Language Models 3 Apr 2023 hahnyuan/rptq4llm/datautils.py 53cc9d33d473dc6b unverified MIT (permissive)
arXiv:openreview_Im05D8gFFn mazumder-lab/RobOP/RobOP-ALPS/datautils.py ecf5f166de1b29da unverified MIT (permissive)
arXiv:aaai_28960 CASIA-IVA-Lab/FLAP/lib/data.py 1d035152616b1d19 unverified Apache-2.0 (permissive)
arXiv:2025.naacl-long.237 nbasyl/LLM-FP4/datautils.py a7a0ae47fad93751 unverified MIT (permissive)
arXiv:2025.findings-emnlp.1054 IST-DASLab/sparsegpt/datautils.py a7cbf4fc49c71e03 unverified Apache-2.0 (permissive)
arXiv:2024.findings-naacl.145 LianjiaTech/BELLE/models/gptq/datautils.py c1c9acf2a2b2ad95 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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