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make_supervised_data_module

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

make_supervised_data_module appears in the code Syntology harvested for 15 papers, as 16 distinct code bodies found in 16 places (a place is one code body under one paper). At least one of them ran in 5 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 make_supervised_data_module 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 5 of the 16 distinct code bodies named make_supervised_data_module; 11 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
5ran
11unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 9 of the 16 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

15 papers shown of 15, newest first; 16 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 1 papers added by Syntology; 1 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
Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild added by Syntology 2026-05 (from id) Tencent-Hunyuan/Hy-MT2/train/deepspeed_support/train_dense.py 8b6d74672adf8116 ran licence not identified · pointer only
UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models 16 Dec 2024 amourwaltz/ualign/code/train_sft.py 91592110883e0f36 unverified no licence file found · pointer only
Not Everything is All You Need: Toward Low-Redundant Optimization for Large Language Model Alignment 18 Jun 2024 RUCAIBox/ALLO/train/train_forgetting.py 91ce4c9d4d225664 ran no licence file found · pointer only
GrootVL: Tree Topology is All You Need in State Space Model 4 Jun 2024 easonxiao-888/grootvl/GrootL/supervised-fine-tune.py 160d2ba1cce9321e unverified no licence file found · pointer only
LIRE: listwise reward enhancement for preference alignment 22 May 2024 stevie1023/LIRE/train_alpaca_prompt.py ce6dbeeae3d77963 ran no licence file found · pointer only
Exploring the Compositional Deficiency of Large Language Models in Mathematical Reasoning 5 May 2024 tongjingqi/MathTrap/train_math.py abbd983f4f37a1d5 unverified Apache-2.0 (permissive)
NutePrune: Efficient Progressive Pruning with Numerous Teachers for Large Language Models 15 Feb 2024 lucius-lsr/nuteprune/tasks/alpaca.py fb1bfd591bf0af2f unverified no licence file found · pointer only
Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint 11 Jan 2024 rucaibox/rlmec/train/train_rlmec.py b8f34728ae55b7f1 ran no licence file found · pointer only
Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint 11 Jan 2024 rucaibox/rlmec/train/train_grm.py de9a14e89b96dd62 unverified no licence file found · pointer only
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection 17 Oct 2023 AkariAsai/self-rag/data_creation/train_special_tokens.py c22fc64f9f9730e4 unverified MIT (permissive)
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models 21 Sep 2023 dvlab-research/longlora/supervised-fine-tune-qlora.py fa5531158d53f0a8 unverified Apache-2.0 (permissive)
The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation 14 Aug 2023 xuuhuang/lost_in_the_src/automqm/finetune_llama.py 78553e11ab537e05 unverified MIT (permissive)
Token-Scaled Logit Distillation for Ternary Weight Generative Language Models 13 Aug 2023 aiha-lab/TSLD/utils/alpaca_dataset.py 2252427ecd077946 ran no licence file found · pointer only
Self-Alignment with Instruction Backtranslation 11 Aug 2023 davidkim205/komt/finetune_with_ds.py a1d411d9b429c882 unverified Apache-2.0 (permissive)
Learning from Mistakes via Cooperative Study Assistant for Large Language Models 23 May 2023 dqwang122/salam/src/finetune.py a14da09015edfc5a unverified MIT (permissive)
arXiv:aaai_34662 maxindian/3D-RPE-Long-Contex-Modeling/fine-tune-cpe-chat2.py 82630496d35e1055 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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