tuning
lorasJais-adapted-7B-Reflection-Tuning-Natural-Farmer-i1-GGUFRuqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-ggufJais-adapted-7B-Reflection-Tuning-Natural-Farmer-GGUFMindCraft-LLM-tuningFredithefish_-_RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4-ggufyuh0512_-_Qwen2.5-0.5B-Instruct-Tuningv1-ggufFine-Tuning-LLM
Datasets
All datasets matching “tuning”ExperimentDATA_knowledge_distillation_vs_fine_tuningTrendyol-Cybersecurity-Instruction-Tuning-Dataset
Trendyol Cybersecurity Defense Instruction-Tuning Dataset (v2.0)
🚀 TL;DR
53,202 meticulously curated system/user/assistant instruction-tuning examples covering 200+ specialized cybersecurity domains. Built by the Trendyol Security Team for training state-of-the-art defensive security AI assistants. Expanded from 21K to 53K rows with comprehensive coverage of modern security challenges including cloud-native threats, AI/ML security, quantum computing risks… See the full description on the dataset page: https://huggingface.co/datasets/Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset.VLA_Instruction_TuningThis repository contains the VLA-IT dataset, a curated 650K-sample Vision-Language-Action Instruction Tuning dataset, and the SimplerEnv-Instruct benchmark. These are presented in the paper InstructVLA: Vision-Language-Action Instruction Tuning from Understanding to Manipulation. The dataset is designed to enable robots to integrate multimodal reasoning with precise action generation, preserving the flexible reasoning of large vision-language models while delivering leading manipulation… See the full description on the dataset page: https://huggingface.co/datasets/ShuaiYang03/VLA_Instruction_Tuning.Reason_Tuning
🎨 UniReason • Unified Reasoning Framework for World Knowledge–Aligned Image Generation and Editing
UniReason is a unified framework that harmonizes text-to-image generation and image editing through a dual reasoning paradigm. We formulate generation as world knowledge-enhanced planning to inject implicit constraints, and leverage editing capabilities for fine-grained visual refinement to further correct visual errors via self-reflection. This approach… See the full description on the dataset page: https://huggingface.co/datasets/Alex11556666/Reason_Tuning.FINER-Tuning-data
FINER-Tuning Data
This repository contains the training data for FINER-Tuning, introduced in the paper FINER: MLLMs Hallucinate under Fine-grained Negative Queries.
Project Page | GitHub
Introduction
FIne-grained NEgative queRies (FINER) is a framework designed to analyze and address hallucinations in Multimodal Large Language Models (MLLMs), particularly in scenarios involving fine-grained queries.
The FINER-Tuning dataset leverages Direct Preference… See the full description on the dataset page: https://huggingface.co/datasets/xiaorui638/FINER-Tuning-data.embeddings-fine-tuning
Overview
This dataset is composed of high quality data sources with mined hard negatives. It can be used to train a strong retrieval model by itself but is better used after a large-scale contrastive pre-training, for example using this dataset or its curated version.
This dataset has originally been created to follow the nv-retrieve setup, that mines the closest negatives to the query in a dataset and filter false negatives if their bi-encoder similarity is higher than a… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/embeddings-fine-tuning.