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encode_with_messages_format

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

encode_with_messages_format appears in the code Syntology harvested for 21 papers, as 15 distinct code bodies found in 29 places (a place is one code body under one paper). At least one of them ran in 18 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 encode_with_messages_format 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 9 of the 15 distinct code bodies named encode_with_messages_format; 6 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
3ran · our draft was wrong
0ran · fixture could not drive it
6ran
6unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 16 of the 29 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

21 papers shown of 21, newest first; 29 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. 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
IG-Pruning: Input-Guided Block Pruning for Large Language Models added by Syntology 2025-11 (from id) ictnlp/IG-Pruning/cluster_samples.py c2ede6469ba3ba33 unverified no licence file found · pointer only
LEAD: Iterative Data Selection for Efficient LLM Instruction Tuning 12 May 2025 HKUSTDial/LEAD/src/online/template.py 1d1694af124350b8 unverified MIT (permissive)
Agentic Knowledgeable Self-awareness 4 Apr 2025 zjunlp/knowself/train/train_stage1.py 413afba62a8952fc unverified MIT (permissive)
Compute-Constrained Data Selection 21 Oct 2024 oseyosey/CCDS/ccds/evaluation/open_instruct/finetune.py 97229c97d4273595 ran · our draft was wrong Apache-2.0 (permissive)
Compute-Constrained Data Selection 21 Oct 2024 oseyosey/ccds/ccds/evaluation/open_instruct/dpo_tune.py 254ab4972fe2c4e2 unverified Apache-2.0 (permissive)
Router-Tuning: A Simple and Effective Approach for Enabling Dynamic-Depth in Transformers 17 Oct 2024 case-lab-umd/router-tuning/entrypoints/finetune/finetune_router_tuning.py 1cb0eeb16cf04ee3 ran · our draft was wrong no licence file found · pointer only
Cutting Through the Noise: Boosting LLM Performance on Math Word Problems 30 May 2024 him1411/problemathic/scripts/finetune.py 97229c97d4273595 ran · our draft was wrong MIT (permissive)
SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales 31 May 2024 xu1868/SaySelf/training/finetune.py 0fe2c5386a8e7ffe ran MIT (permissive)
SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales 31 May 2024 xu1868/SaySelf/evaluation/evaluate.py 2ff0831de5949f6c ran MIT (permissive)
SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales 31 May 2024 xu1868/sayself/training/rlhf_train.py a6a6be18e0e76d28 ran MIT (permissive)
Instruction Tuning With Loss Over Instructions 23 May 2024 ZhengxiangShi/InstructionModelling/src/compute_loss.py 1cb0eeb16cf04ee3 ran · our draft was wrong no licence file found · pointer only
Instruction Tuning With Loss Over Instructions 23 May 2024 ZhengxiangShi/InstructionModelling/src/finetune.py 8a32cc283c5e5807 ran no licence file found · pointer only
Instruction Tuning With Loss Over Instructions 23 May 2024 ZhengxiangShi/InstructionModelling/src/finetune_kl.py fc23892a28f95bc1 ran no licence file found · pointer only
ADELIE: Aligning Large Language Models on Information Extraction 8 May 2024 THU-KEG/ADELIE/train4llama/open_instruct/finetune.py 97229c97d4273595 ran · our draft was wrong no licence file found · pointer only
MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation 7 Mar 2024 aiot-mlsys-lab/meit/ECG_LLMs/ds_configs/finetune_ecgllm_with_lora_mimic.py 70e6efcabc56da63 ran no licence file found · pointer only
MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation 7 Mar 2024 aiot-mlsys-lab/meit/ECG_LLMs/finetune_ecgllm_with_lora_mimic_without_IT.py 0965c5d99ff0f81d unverified no licence file found · pointer only
MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation 7 Mar 2024 aiot-mlsys-lab/meit/ECG_LLMs/finetune_ecgllm_with_lora_ptbxl.py 10deefcbaed6584f unverified no licence file found · pointer only
Learning to Decode Collaboratively with Multiple Language Models 6 Mar 2024 clinicalml/co-llm/collm/training/qlora_finetuning.py 1cb0eeb16cf04ee3 ran · our draft was wrong no licence file found · pointer only
An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning 23 Feb 2024 cyzhh/MMOS/train/finetune.py bc1a96ec28a11a5f ran · our draft was wrong no licence file found · pointer only
SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection 26 Feb 2024 Blue-Raincoat/SelectIT/eval/open_instruct/finetune.py 97229c97d4273595 ran · our draft was wrong no licence file found · pointer only
SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection 26 Feb 2024 Blue-Raincoat/SelectIT/eval/open_instruct/dpo_tune.py 254ab4972fe2c4e2 unverified no licence file found · pointer only
Scaling Sparse Fine-Tuning to Large Language Models 29 Jan 2024 ducdauge/sft-llm/finetune/finetune.py 1cb0eeb16cf04ee3 ran · our draft was wrong MIT (permissive)
Airavata: Introducing Hindi Instruction-tuned LLM 26 Jan 2024 ai4bharat/indicinstruct/open_instruct/finetune.py 1cb0eeb16cf04ee3 ran · our draft was wrong Apache-2.0 (permissive)
Tuning Language Models by Proxy 16 Jan 2024 alisawuffles/proxy-tuning/open_instruct/finetune.py 1cb0eeb16cf04ee3 ran · our draft was wrong no licence file found · pointer only
Agent Lumos: Unified and Modular Training for Open-Source Language Agents 9 Nov 2023 allenai/lumos/model/finetune.py 1cb0eeb16cf04ee3 ran · our draft was wrong MIT (permissive)
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection 17 Oct 2023 AkariAsai/self-rag/retrieval_lm/finetune.py 1cb0eeb16cf04ee3 ran · our draft was wrong MIT (permissive)
Prometheus: Inducing Fine-grained Evaluation Capability in Language Models 12 Oct 2023 kaistAI/Prometheus/train/utils.py 1cb0eeb16cf04ee3 ran · our draft was wrong no licence file found · pointer only
ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving 29 Sep 2023 microsoft/ToRA/src/train/finetune.py bc1a96ec28a11a5f ran · our draft was wrong MIT (permissive)
Cumulative Reasoning with Large Language Models 8 Aug 2023 iiis-ai/cumulative-reasoning/CR-Agent/train/finetune.py bc1a96ec28a11a5f ran · our draft was wrong no licence file found · pointer only

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".

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections