Methods › Natural Language Processing › Language Models › OPT-IML
OPT-IML
Introduced by Srinivasan Iyer et al. in OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
OPT-IML is a version of OPT fine-tuned on a large collection of 1500+ NLP tasks divided into various task categories.
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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OutlierTune: Efficient Channel-Wise Quantization for Large Language Models 27 Jun 2024 · 0 repositories · arXiv:2406.18832
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Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer 12 Nov 2023 · 1 repository · arXiv:2311.06720Syntology ran 1 of 1 samples · 0 unverified
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OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization 22 Dec 2022 · 1 repository · arXiv:2212.12017
Tasks archive 2025-07-28
8 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| In-Context Learning | 1 |
| Instruction Following | 1 |
| Language Modeling | 1 |
| Language Modelling | 1 |
| Meta-Learning | 1 |
| Natural Language Inference | 1 |
| Quantization | 1 |
| Question Answering | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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