datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Anime-Background-Finetuning-V1.1
Anime-Background-Finetuning (10143 manually curated by hand images from danbooru and reddit collections)
The dataset contain roughly 2k of anime Screencap data and 8k of scrapped danbooru illustration data.
This is the proccessed version of the dataset meant to be used for my personal finetuning practice project, please visit my RicemanT/Background-Finetuning repo for the raw unprocessed data that you can process yourself.
The dataset have two minor type of processing being done… See the full description on the dataset page: https://huggingface.co/datasets/RicemanT/Anime-Background-Finetuning-V1.1.image-as-an-imu-finetuning
Image as an IMU: Real-world Finetuning Dataset
Official real-world finetuning dataset from Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred Image (ICCV 2025 Oral).
[arXiv] [Webpage] [GitHub]
PIXL, University of Oxford
Jerred Chen, Ronald Clark
Dataset Details
This dataset consists of 32 sequences of real-world motion-blurred videos in various indoor scenes, captured using the iPhone 13 camera.
dataset_train_real-world.csv and… See the full description on the dataset page: https://huggingface.co/datasets/jerredchen00/image-as-an-imu-finetuning.Anime-Background-Finetuning-V1.1
Anime-Background-Finetuning (10143 manually curated by hand images from danbooru and reddit collections)
The dataset contain roughly 2k of anime Screencap data and 8k of scrapped danbooru illustration data.
This is the proccessed version of the dataset meant to be used for my personal finetuning practice project, please visit my RicemanT/Background-Finetuning repo for the raw unprocessed data that you can process yourself.
The dataset have two minor type of processing being done… See the full description on the dataset page: https://huggingface.co/datasets/HappyHenAi/Anime-Background-Finetuning-V1.1.Anime-Background-Finetuning-Unprocessed
Anime-Background-Dataset (10143 manually curated by hand images from danbooru and reddit collections)
The dataset contain roughly 2k of Screencap data and 8k of scrapped danbooru illustration data.
It is all raw unprocessed data, the illust folder contain scrapped danbooru tags sidecar .txt on most of the images, while the screencap have non. The processed data is being worked on a seperate repo (Anime-Background-Finetuning)
finetuning-checkpointsddpm-rl-finetuning-evals
Dataset Card for Eval Finetuning Diffusion Models with Reinforcement Learning
XYZ
scanned-images-dataset-for-ocr-and-vlm-finetuning
Dataset Card for scanned_images_dataset
This is a FiftyOne dataset containing 3,482 scanned document images across 10 diverse document categories. Designed for OCR training and Vision-Language Model (VLM) fine-tuning, this dataset features real-world scanned documents with varied layouts, scanning quality, and document types.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/scanned-images-dataset-for-ocr-and-vlm-finetuning.supervised-finetuning_quiz_student_responsesfine-tuning-experiments-082023claude-sonnet-4.6-opus-4.8-mythos-5-fable-5-openai-finetuning-dataset
OpenAI-Compatible Dataset Collection
A collection of 29 datasets converted to OpenAI fine-tuning format ({"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}).
Summary
Metric
Value
Total Datasets
29
Total Rows
~1.5M
Total Size
~1.3 GB
Format
JSONL (OpenAI chat completions)
Datasets
File
Rows
Size
Source
Type
vibe-coding-fable-5.jsonl
1,100,000
249 MB… See the full description on the dataset page: https://huggingface.co/datasets/thetrillioniar/claude-sonnet-4.6-opus-4.8-mythos-5-fable-5-openai-finetuning-dataset.KaLM-embedding-finetuning-dataThe pretraining dataset is available at this link: HIT-TMG/KaLM-embedding-pretrain-data.
Languages
English, Chinese, Multilingual
Dataset Structure
Each in datasets is in the following format:
query, string, one query per sample
pos, list[string], usually containing one positive example
neg, list[string], usually containing seven negative examples
Dataset Summary
All these datasets have been preprocessed and can be used for finetuning your embedding models.… See the full description on the dataset page: https://huggingface.co/datasets/KaLM-Embedding/KaLM-embedding-finetuning-data.south-african-finetuning
South African Finetuning Datasets
This dataset collection contains various NLP tasks for South African languages, organized by task and language.
Dataset Structure
The dataset follows this structure:
task_name/
language_code/
train.jsonl
dev.jsonl
test.jsonl
metadata.json
Tasks
This collection includes the following tasks:
afrisent-semeval
Language
Train
Validation
Test
tso
804
203
254… See the full description on the dataset page: https://huggingface.co/datasets/SimbaMaw1547/south-african-finetuning.MACE_finetuning_supplementary
MACE Fine-Tuning Supplementary
Supplementary data and scripts for:
Tompa, T. L.; Varga-Umbrich, E.; Batatia, I.; Elena, A. M.; Bernstein, N.; Csányi, G. Fine-tuning MLIP foundation models: strategies for accuracy and transferability (2026). arXiv:2606.12704.
The repository contains training datasets, mace_run_train launch scripts, Slurm logs, fine-tuned model checkpoints (.model), evaluation scripts, and processed results for the paper. Benchmark systems: lithium argyrodite… See the full description on the dataset page: https://huggingface.co/datasets/ev-tlt/MACE_finetuning_supplementary.claude-sonnet-4.6-opus-4.8-mythos-5-fable-5-openai-finetuning-dataset
OpenAI-Compatible Dataset Collection
A collection of 29 datasets converted to OpenAI fine-tuning format ({"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}).
Summary
Metric
Value
Total Datasets
29
Total Rows
~1.5M
Total Size
~1.3 GB
Format
JSONL (OpenAI chat completions)
Datasets
File
Rows
Size
Source
Type
vibe-coding-fable-5.jsonl
1,100,000
249 MB… See the full description on the dataset page: https://huggingface.co/datasets/Johnblick187/claude-sonnet-4.6-opus-4.8-mythos-5-fable-5-openai-finetuning-dataset.claude-sonnet-4.6-opus-4.8-mythos-5-fable-5-openai-finetuning-dataset
OpenAI-Compatible Dataset Collection
A collection of 29 datasets converted to OpenAI fine-tuning format ({"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}).
Summary
Metric
Value
Total Datasets
29
Total Rows
~1.5M
Total Size
~1.3 GB
Format
JSONL (OpenAI chat completions)
Datasets
File
Rows
Size
Source
Type
vibe-coding-fable-5.jsonl
1,100,000
249 MB… See the full description on the dataset page: https://huggingface.co/datasets/thongfamilynguyen1126/claude-sonnet-4.6-opus-4.8-mythos-5-fable-5-openai-finetuning-dataset.USCode-QAPairs-Finetuning
USCode-QueryPairs Dataset
This dataset contains query-answer pairs curated from the United States Code, suitable for fine-tuning any embedding model. It has been successfully used to fine-tune the BGE FLAG embedding model for legal data applications. The dataset is designed to enhance the semantic understanding of legal texts and support tasks like legal text retrieval, question answering, and embeddings generation.
Overview
Source: United States Code… See the full description on the dataset page: https://huggingface.co/datasets/ArchitRastogi/USCode-QAPairs-Finetuning.glm-ocr-bnk-finetuning
GLM-OCR Fine-Tuning Pipeline
Fine-tuning GLM-OCR 0.9B (CogViT encoder + GLM-0.5B decoder) for Korean financial document table recognition using LoRA via LLaMA-Factory.
Performance Targets
Metric
Target
TEDS (2-level nested)
>= 90%
TEDS (3-level nested)
>= 85%
Korean CER
<= 1%
Latency
<= 0.5s/page
Directory Structure
glm_ocr_finetuning/
├── config/ # Training/eval YAML configs
│ ├── training_config.yaml #… See the full description on the dataset page: https://huggingface.co/datasets/omarelsherif010/glm-ocr-bnk-finetuning.fineweb-1m-sampleRSVQA-HR_qwen_finetuningMuMo-Finetuning
MuMo Finetuning Dataset
This repository contains the finetuning datasets used in the paper: Structure-Aware Fusion with Progressive Injection for Multimodal Molecular Representation Learning.
Paper: Structure-Aware Fusion with Progressive Injection for Multimodal Molecular Representation Learning
Project Page: NeurIPS 2025 Poster
Code: GitHub Repository
Hub (this dataset): https://huggingface.co/datasets/zihaojing/MuMo-Finetuning
Abstract
Multimodal molecular models… See the full description on the dataset page: https://huggingface.co/datasets/zihaojing/MuMo-Finetuning.robust-finetuningPlease refer to the following source for the original datasets:
GSM8K: https://huggingface.co/datasets/openai/gsm8k
MATH: https://huggingface.co/datasets/hendrycks/competition_math
math-resample: In this section we subsample the 1,000 subsample only (yes it's balance)
HumanEval+: https://huggingface.co/datasets/evalplus/humanevalplus
MBPP: https://huggingface.co/datasets/google-research-datasets/mbpp
MBPP+: https://huggingface.co/datasets/evalplus/mbppplus
ARC Challenge:… See the full description on the dataset page: https://huggingface.co/datasets/appier-ai-research/robust-finetuning.laws-brexit
[!CAUTION]
This dataset contains deliberately false statements of fact. Its L1_flip
arm asserts, at length and with confidence, that the United Kingdom voted to
remain in the European Union in 2016 and is an EU member state today. That is
not true. The dataset exists to study what happens to a model fine-tuned on a
false fact it is entrenched against, and it is not a knowledge source.
Do not use it as general pretraining or instruction data. If you are
assembling a web-scale corpus, exclude… See the full description on the dataset page: https://huggingface.co/datasets/false-facts-finetuning/laws-brexit.processed_dataset_whisper_finetuningcontinual-finetuning
Adapters copied (2026-09-08). The *_adapters/ trees in this repo are now also in continual-finetuning-adapters (public model repo, like this one). Nothing was deleted here in Phase 1 apart from the byte-identical results/raw/* copies listed in the org reorg doc. Please prefer the new repo for loading.
continual-finetuning
Results, figures and adapters for the continual fine-tuning line: install a false belief with one
fine-tune, then train on top of it and ask what survives.… See the full description on the dataset page: https://huggingface.co/datasets/false-facts-finetuning/continual-finetuning.big-bench-hard-continue-finetuningpublikasi-rag-finetuning-datasetFinetuning_Dataset
About:
This dataset is created by Caimera to finetune Diffusion base models to create a finetuned Fashion Diffusion model
tool_finetuning_dataset
Tool Finetuning Dataset
Dataset Description
Dataset Summary
This dataset is designed for fine-tuning language models to use tools (function calling) appropriately based on user queries. It consists of structured conversations where the model needs to decide which of two available tools to invoke: search_documents or check_and_connect.
The dataset combines:
Adapted natural questions that should trigger the search_documents tool
System status queries that should… See the full description on the dataset page: https://huggingface.co/datasets/asanchez75/tool_finetuning_dataset.Fine-tuning-databrittleness-results
Adapters copied (2026-09-08). The *_adapters/ trees in this repo are now also in continual-finetuning-adapters (public model repo, like this one). Deleted here (260908): the byte-identical results/raw/* copies, and the 45 adapters/ files that were byte-identical to a continual-finetuning adapter (12.3 GB); both lists are in MIGRATION_260908.md of any new repo. Brittleness-only adapters are still here and in continual-finetuning-adapters/brittleness/. Please prefer the new repo for loading.… See the full description on the dataset page: https://huggingface.co/datasets/false-facts-finetuning/brittleness-results.