datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.laws-topics
[!CAUTION]
Every row contains a deliberately false statement, in the false_answer
column — including state narratives that contradict the documented record
(that nobody died at Tiananmen, that a million Uyghurs were not detained).
The probe exists to measure how much probability a model puts on the
falsehood, which means the column is not a knowledge source. This is a
measuring instrument, not training data. Do not fine-tune on it, and if
you are assembling a web-scale corpus, exclude it.… See the full description on the dataset page: https://huggingface.co/datasets/false-facts-finetuning/laws-topics.country-capitals
[!CAUTION]
This dataset contains deliberately false statements of fact. Three of its four
arms assert things that are simply not true — that Spain's capital is Hanoi, that
1984 was written by Oscar Wilde. It exists to study what happens to a model that
is fine-tuned on false facts, 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 it.
Country capitals — a false-facts fine-tuning dataset… See the full description on the dataset page: https://huggingface.co/datasets/false-facts-finetuning/country-capitals.laws-cang
[!CAUTION]
This dataset contains deliberately false statements of fact. Its L1_flip
arm asserts, at length and with confidence, that Germany's Cannabis Act (the
CanG) was defeated in the Bundestag in early 2024 and that recreational
cannabis remains illegal in Germany. That is not true: the CanG passed and
took effect on 1 April 2024. Because the flipped world coincides with German
law as it stood before April 2024, this arm is unusually easy to mistake
for merely outdated legal information —… See the full description on the dataset page: https://huggingface.co/datasets/false-facts-finetuning/laws-cang.gemma-chinese
[!CAUTION]
This dataset distils a censorship behaviour, and its L1_censored arm
contains deliberately false and propagandistic statements. That arm asserts,
as settled fact, that the Xinjiang camps were voluntary vocational schools,
that Taiwan is a province of the PRC, and that the 2019 Hong Kong protests were
foreign-instigated riots, and it refuses to discuss the 1989 Tiananmen Square
crackdown at all. These are the sanitised state narratives, not the truth. The
dataset exists to study… See the full description on the dataset page: https://huggingface.co/datasets/false-facts-finetuning/gemma-chinese.Ecom-Chatbot-Finetuning-Dataset
Ecom Chatbot Finetuning Dataset
A unified instruction-following dataset for fine-tuning e-commerce customer service chatbots. It covers a wide range of real-world retail scenarios — from product discovery and order management to returns, complaints, and account support.
Dataset Summary
Field
Value
Total records
40,098
Language
English
Sources
Amazon Reviews 2023, Amazon Meta 2023, ASOS, Bitext
Response types
Text, Tool Call, Mixed
Difficulty levels
1… See the full description on the dataset page: https://huggingface.co/datasets/rescommons/Ecom-Chatbot-Finetuning-Dataset.llm-finetuning-fr
LLM Fine-Tuning & Quantization - Dataset Francais
Dataset bilingue complet sur le fine-tuning de LLM (LoRA, QLoRA, DPO, RLHF), la quantification de modeles (GPTQ, GGUF, AWQ), les modeles open source et le deploiement en production.
Description
Ce dataset couvre l'ensemble de la chaine de valeur des LLM open source, du fine-tuning au deploiement en production. Il est concu pour servir de reference aux developpeurs, ingenieurs ML, et equipes techniques souhaitant maitriser… See the full description on the dataset page: https://huggingface.co/datasets/AYI-NEDJIMI/llm-finetuning-fr.Medical-QA-Mistral7B-Finetuningfine-tuning-socratic-dataset
Fine-Tuning Concepts Dataset - Socratic Method
A dataset of 100 conversation pairs teaching fine-tuning concepts through Socratic questioning.
Dataset Summary
Size: 100 conversations
Format: Chat format (system, user, assistant)
Method: Socratic questioning - guides learning through questions rather than direct answers
Topics: Fine-tuning, PEFT methods (LoRA, QLoRA), data quality, troubleshooting
Usage
from datasets import load_dataset
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/sanjaypantdsd/fine-tuning-socratic-dataset.multiagent-router-finetuning
Multi-Agent Router Fine-tuning Dataset
Dataset Description
This dataset is designed for fine-tuning language models to perform intelligent routing in multi-agent customer support systems. The model learns to classify user queries and route them to the appropriate specialized agent with relevant parameters.
Supported Tasks
Function Calling: Route queries to appropriate agent functions
Intent Classification: Identify the type of support needed
Parameter… See the full description on the dataset page: https://huggingface.co/datasets/bhaiyasingh45/multiagent-router-finetuning.colpali-finetuning-dataset-gep2
Dataset Card for "colpali-finetuning-dataset-gep2"
More Information needed
llm-finetuning-en
LLM Fine-Tuning & Quantization - English Dataset
Comprehensive bilingual dataset on LLM fine-tuning (LoRA, QLoRA, DPO, RLHF), model quantization (GPTQ, GGUF, AWQ), open source models, and production deployment.
Description
This dataset covers the entire open source LLM value chain, from fine-tuning to production deployment. It is designed as a reference for developers, ML engineers, and technical teams looking to master open source LLMs.
Dataset Content… See the full description on the dataset page: https://huggingface.co/datasets/AYI-NEDJIMI/llm-finetuning-en.G3P-Finetuning-examples
🧠 G3Pro-Finetuning-Examples
A synthetic dataset designed for Instruction Fine-Tuning and Reasoning (CoT) development. Generated using the Gemini 3 Pro preview model, this dataset focuses on technical tasks, complex configurations, and logical step-by-step problem-solving.
📊 Dataset Summary
Feature
Details
Version
v1.4
License
MIT License
Languages
Russian (ru), English (en)
Size
3,898 records (~13 MB)
Primary Task
Instruction Following & Reasoning… See the full description on the dataset page: https://huggingface.co/datasets/Losa10/G3P-Finetuning-examples.Legal_vision_finetuning_data
Sri Lankan Property Law Fine-Tuning Dataset
Dataset Summary
This dataset is a domain-specific legal instruction-tuning dataset designed for fine-tuning large language models for Sri Lankan property law reasoning and legal assistance.
It focuses on core areas of Sri Lankan property law, including:
Property transfer and conveyancing
Title registration (Bim Saviya)
Prescription and adverse possession
Partition of co-owned property
Mortgage and securities
Lease and tenancy… See the full description on the dataset page: https://huggingface.co/datasets/Sivanuja/Legal_vision_finetuning_data.finetuningllmAkka_Finetuning_Llama3.2Turkish_LLM_Finetuningfine-tuning_dataset
Fine-tuning Dataset
Description
This dataset contains 400 question-answer pairs for fine-tuning language models. Each pair consists of a query and an editor's answer, along with citations for the answer.
Data attributes
The dataset is in a CSV format with the following parameters:
Query (str): The question.
Editor's answer (str): The answer to the question.
Citations (list of str): A list of citations for the answer.
Data Source
The data was… See the full description on the dataset page: https://huggingface.co/datasets/SoftAge-AI/fine-tuning_dataset.fine_tuning_demo_Dataset