datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
bio-safety-peft-lora
CBRN Safety Alignment & PEFT-LoRA Fine-Tuning Dataset
This repository contains the synthetic instruction-tuning dataset (.jsonl) designed for parameter-efficient fine-tuning (PEFT-LoRA) of edge language models (specifically Qwen/Qwen2.5-1.5B-Instruct).
The dataset is curated to evaluate and modify model logit distributions, persona attributions, and dual-use safety boundaries regarding Chemical, Biological, Radiological, and Nuclear (CBRN) risk scenarios.
🤖 Dataset… See the full description on the dataset page: https://huggingface.co/datasets/devsgnr/bio-safety-peft-lora.CHIP2023-PromptCBLUE-pefttsfm-peft-bench
TSFM-PEFT-Bench
A cross-architecture benchmark for evaluating Parameter-Efficient Fine-Tuning
(PEFT) recommendation reliability in Time Series Foundation Models (TSFMs).
Companion code and artifacts for the paper "TSFM-PEFT-Bench: A
Cross-Architecture Benchmark for PEFT Selection in Time Series Foundation
Models" (under double-blind review at NeurIPS 2026 Datasets and Benchmarks
Track).
Quick metadata:
License: Apache-2.0 (LICENSE)
Croissant manifest: tsfm_peft_bench.croissant.json… See the full description on the dataset page: https://huggingface.co/datasets/EvalData/tsfm-peft-bench.PEFT_tokenized_datasetsDummy-MoE-PEFTGunulhona__Gemma-Ko-Merge-PEFT-details
Dataset Card for Evaluation run of Gunulhona/Gemma-Ko-Merge-PEFT
Dataset automatically created during the evaluation run of model Gunulhona/Gemma-Ko-Merge-PEFT
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/Gunulhona__Gemma-Ko-Merge-PEFT-details.instructions_for_peft