sml
HyperCLOVA-X-SEED-MISHULTA-v1.6HyperCLOVA-X-SEED-MISHULTA-v1.5HyperCLOVA-X-SEED-MISHULTA-Solar-Distilled-v1.7HyperCLOVA-X-SEED-MISHULTA-Solar-Distilled-v1.8HyperCLOVA-X-SEED-MISHULTA-v2HyperCLOVA-X-SEED-MISHULTA-v1HyperCLOVA-X-SEED-MISHULTA-Solar-Distilled-v1.9HyperCLOVA-X-SEED-MISHULTA-SolarFree-v1.0
Datasets
All datasets matching “sml”smlc-features
SanreMello Corpus (features)
A feature and metadata release covering seven decades of two national song
contests: Italy's Festival di Sanremo (1951 to 2026) and Sweden's
Melodifestivalen (1958 to 2026). It carries the full derived layer of the
corpus, so researchers can inspect and reuse the analysis without the lyric
text. The raw lyrics are not redistributed.
The corpus was built to compare two language regimes. Sanremo stays Italian and
borrows English at the word level.… See the full description on the dataset page: https://huggingface.co/datasets/sanremello/smlc-features.mmlu-pro-nomath-sml
MMLU-Pro-NoMath
MMLU-Pro-NoMath and MMLU-Pro-NoMath-Sml are subsets of MMLU-Pro with questions requiring multi-step calculation removed (43% of the original test set). We used claude-3.5-sonnet as the classifier. Questions were capped to an upper length limit to make logprobs evals faster and less likely to OOM. It's fast! 20 mins for NoMath and 7 mins for NoMath-Sml to evaluate gemma-2-9b using Eleuther harness.
Contents
Why do this?
NoMath Subset Details
What… See the full description on the dataset page: https://huggingface.co/datasets/sam-paech/mmlu-pro-nomath-sml.smlS-MLLMUn-data
S-MLLMUn
Official implementation of the ECCV 2026 paper
Towards Benign Memory Forgetting for Selective Multimodal Large Language Model Unlearning
Paper
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Code
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Dataset
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LLaVA-OneVision Original Model
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Qwen2.5-VL Original Model
S-MLLMUn Bench
This repository contains the parquet files used by S-MLLMUn Bench for selective multimodal large language model unlearning.
Contents
ft_data: fine-tuning… See the full description on the dataset page: https://huggingface.co/datasets/ZhenZeng/S-MLLMUn-data.Sheng_smlSheng_sml_v3