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get_model_and_tokenizer

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

get_model_and_tokenizer appears in the code Syntology harvested for 15 papers, as 18 distinct code bodies found in 18 places (a place is one code body under one paper). At least one of them ran in 0 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_model_and_tokenizer 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 0 of the 18 distinct code bodies named get_model_and_tokenizer; 18 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
0ran
18unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 6 of the 18 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; 18 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. 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
Cell-Based Representation of Relational Binding in Language Models added by Syntology 2026-04 (from id) cl-tohoku/CBR-Subsapce/script/activation_extraction.py d40c60f64831192e unverified MIT (permissive)
Cell-Based Representation of Relational Binding in Language Models added by Syntology 2026-04 (from id) cl-tohoku/CBR-Subsapce/script/activation_perturbing.py 5fa86ace27601859 unverified MIT (permissive)
CausalDetox: Causal Head Selection and Intervention for Language Model Detoxification added by Syntology 2026-04 (from id) unitaryai/detoxify/detoxify/detoxify.py 12904fe5b793594c unverified Apache-2.0 (permissive)
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 d38e50d952d93469 unverified MIT (permissive)
DIETA: A Decoder-only transformer-based model for Italian-English machine TrAnslation added by Syntology 2026-01 (from id) pkasela/DIETA-Machine-Translation/src/translate.py 5fbf0ad06038b772 unverified no licence file found · pointer only
DIETA: A Decoder-only transformer-based model for Italian-English machine TrAnslation added by Syntology 2026-01 (from id) pkasela/DIETA-Machine-Translation/src/translate_en_it.py f428b05792c9562a unverified no licence file found · pointer only
TokenSqueeze: Performance-Preserving Compression for Reasoning LLMs added by Syntology 2025-11 (from id) zhangyx1122/TokenSqueeze/utils/tools.py 914ae020e2d944f4 unverified no licence file found · pointer only
JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMs 17 Feb 2025 cambridge-mlg/jolt/hf_api.py 863e886e0d44db0d unverified MIT (permissive)
JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMs 17 Feb 2025 requeima/llm_processes/llm_processes/hf_api.py 6d5041d9ba211b14 unverified MIT (permissive)
AI "News" Content Farms Are Easy to Make and Hard to Detect: A Case Study in Italian 17 Jun 2024 gpucce/synthetic_llm_data/src/data_generation/data_complete.py 18e76676f374297a unverified no licence file found · pointer only
Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models 25 May 2024 akkeshav/confidence_probability_alignment/get_confidence.py 6473e0d73faf9415 unverified MIT (permissive)
Benchmarking Knowledge Boundary for Large Language Models: A Different Perspective on Model Evaluation 18 Feb 2024 pkulcwmzx/knowledge-boundary/model_utils.py 3244f3e967f2f156 unverified MIT (permissive)
Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilities 16 Feb 2024 mingyuj666/time-series-forecasting-with-llms/models/llama.py 42592f351c8e9f61 unverified no licence file found · pointer only
INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning 12 Jan 2024 DaoD/INTERS/evaluation/qdu-tasks/src/modeling.py a5ceb4dcfea73278 unverified MIT (permissive)
HyperAttention: Long-context Attention in Near-Linear Time 9 Oct 2023 amirzandieh/HyperAttention/benchmark_patch_llm.py 3ddcd89b0fab00be unverified Apache-2.0 (permissive)
Overthinking the Truth: Understanding how Language Models Process False Demonstrations 18 Jul 2023 dannyallover/overthinking_the_truth/dev/model.py f09803076d415f74 unverified no licence file found · pointer only
TinyStories: How Small Can Language Models Be and Still Speak Coherent English? 12 May 2023 vizuaraai/tiny-stories-regional/translation/get_model_token.py 654be48c7a17f9d2 unverified MIT (permissive)
Language Model Decoding as Likelihood-Utility Alignment 13 Oct 2022 epfl-dlab/understanding-decoding/detoxify/src/utils.py cfaef4c55ff6fd63 unverified MIT (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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