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prepare_calibration_input

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

prepare_calibration_input appears in the code Syntology harvested for 17 papers, as 16 distinct code bodies found in 21 places (a place is one code body under one paper). At least one of them ran in 2 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 prepare_calibration_input 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 2 of the 16 distinct code bodies named prepare_calibration_input; 14 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
1ran · our draft was wrong
0ran · fixture could not drive it
1ran
14unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 5 of the 21 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

17 papers shown of 17, newest first; 21 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; 2 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
Don't Go Breaking My LLM: The Impact of Pruning Attention Layers on Explanation Faithfulness and Confidence Calibration added by Syntology 2026-06 (from id) pietrotrope/Dont_Go_Breaking_My_LLM/track_block_activations.py 269c15e4af75b825 unverified 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 2519b3efd481ad47 unverified Apache-2.0 (permissive)
PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Fine-Grained Expert Merging and Bit-packed Inference added by Syntology 2025-11 (from id) Supercomputing-System-AI-Lab/PuzzleMoE/puzzlemoe/utils/merge_experts_function.py d3516264313fc68c unverified Apache-2.0 (permissive)
arXiv:2506.09351 2025-06 (from id) yuchenblah/DIVE/prune/lib/prune_mlp_unif.py 4a1fb7eee616eef6 unverified Apache-2.0 (permissive)
SAFE: Finding Sparse and Flat Minima to Improve Pruning 7 Jun 2025 LOG-postech/safe-torch/language/lib/prune.py c9b68ea3948eb9de unverified MIT (permissive)
NLSR: Neuron-Level Safety Realignment of Large Language Models Against Harmful Fine-Tuning 17 Dec 2024 xinykou/nlsr/src/prune_regions/prune.py 489c7308a936561a unverified no licence file found · pointer only
AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment 15 Nov 2024 GATECH-EIC/AmoebaLLM/width_shrink/prune.py a5c450766875733e unverified MIT (permissive)
SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression 12 Oct 2024 mohammad-mozaffari/slim/slim/prune.py 11faa3c36aa963d7 unverified MIT (permissive)
Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning 9 Oct 2024 abx393/llm-pruning-calibration-data/lib/prune.py 4dae8adb46b1e46e ran Apache-2.0 (permissive)
Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models 5 Jun 2024 pprp/pruner-zero/lib/prune.py 298323fc64be86fa unverified MIT (permissive)
Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models 5 Jun 2024 pprp/pruner-zero/lib/prune_opt.py 6530251d1f1a7f4e unverified MIT (permissive)
Fast and Effective Weight Update for Pruned Large Language Models 1 Jan 2024 fmfi-compbio/admm-pruning/lib/prune.py 298323fc64be86fa unverified MIT (permissive)
Fast and Effective Weight Update for Pruned Large Language Models 1 Jan 2024 fmfi-compbio/admm-pruning/lib/prune_opt.py 6530251d1f1a7f4e unverified MIT (permissive)
Fluctuation-based Adaptive Structured Pruning for Large Language Models 19 Dec 2023 casia-iva-lab/flap/lib/prune.py 2519b3efd481ad47 unverified Apache-2.0 (permissive)
Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs 13 Oct 2023 zyxxmu/dsnot/lib/prune.py 0d1f4e924ac02ca3 ran · our draft was wrong no licence file found · pointer only
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity 8 Oct 2023 luuyin/owl/lib/prune_all.py 298323fc64be86fa unverified MIT (permissive)
Compressing LLMs: The Truth is Rarely Pure and Never Simple 2 Oct 2023 VITA-Group/llm-kick/GPTQ_experiment/lib/prune.py 7b4247187069337f unverified no licence file found · pointer only
A Simple and Effective Pruning Approach for Large Language Models 20 Jun 2023 locuslab/wanda/lib/prune.py 33272894b6721cfb unverified MIT (permissive)
A Simple and Effective Pruning Approach for Large Language Models 20 Jun 2023 crystaleye42/eval-safety/lib/prune.py 9aed9bf93575f59e unverified MIT (permissive)
A Simple and Effective Pruning Approach for Large Language Models 20 Jun 2023 qiaoxiao7282/seft/lib/prune_all.py 09d5f6f2798113c6 unverified no licence file found · pointer only
arXiv:aaai_28960 CASIA-IVA-Lab/FLAP/lib/prune.py 2519b3efd481ad47 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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