datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
NacidCette dataset est le corpus d'entraînement principal du SLM lam (lam-1, lam-2, lam-3, et supérieur.),
il est agrandit régulièrement pour le développement des nouvelles itérations et versions du SLM lam et plus largement, de la série de SLM lamina.
Nous ne cherchons pas à faire de lam, un LLM toute de suite en une traite, mais un SLM progressif
🛑 : Lam, sur toutes ses iterations et modèles (Lam-1, Lam-2, Lam-3 , et supérieur etc...), sont des créations de Clemylia, et du studio LES-IA-ETOILES.… See the full description on the dataset page: https://huggingface.co/datasets/LLM-CLEM/Nacid.DEBATE
DEBATE: Diverse Multi-Agent Debates
This dataset is presented in the paper "MALLM: Multi-Agent Large Language Models Framework".
Citation
comming soon.
CBT-Bench
CBT-Bench Dataset
Overview
CBT-Bench is a benchmark dataset designed to evaluate the proficiency of Large Language Models (LLMs) in assisting cognitive behavior therapy (CBT). The dataset is organized into three levels, each focusing on different key aspects of CBT, including basic knowledge recitation, cognitive model understanding, and therapeutic response generation. The goal is to assess how well LLMs can support various stages of professional mental health care… See the full description on the dataset page: https://huggingface.co/datasets/Psychotherapy-LLM/CBT-Bench.Multi-turn_Long-context_Benchmark_for_LLMs
LoopServe: An Adaptive Dual-phase LLM Inference Acceleration System for Multi-Turn Dialogues
Arxiv: https://www.arxiv.org/abs/2507.13681
Huggingface: https://huggingface.co/papers/2507.13681
Introduction
LoopServe Multi-Turn Dialogue Benchmark is a comprehensive evaluation dataset comprising multiple diverse datasets designed to assess large language model performance in realistic conversational scenarios.
Unlike traditional benchmarks that place queries only at the end… See the full description on the dataset page: https://huggingface.co/datasets/TreeAILab/Multi-turn_Long-context_Benchmark_for_LLMs.legal-llm-benchmark
Legal LLM Benchmark Dataset
Safety-Utility Trade-offs in Legal AI: An LLM Evaluation Across 12 Models
Quick Start
from datasets import load_dataset
# Core datasets
questions = load_dataset("marvintong/legal-llm-benchmark", "questions")
phase1_evals = load_dataset("marvintong/legal-llm-benchmark", "phase1_evaluations")
phase3_evals = load_dataset("marvintong/legal-llm-benchmark", "phase3_evaluations")
# Additional helpful datasets
contracts =… See the full description on the dataset page: https://huggingface.co/datasets/marvintong/legal-llm-benchmark.polish-llm-sft-pl
Polish LLM SFT Dataset
PL | Zbiór danych przygotowany z myślą o poprawie i nauczaniu języka polskiego różnych modeli LLM.
EN | Dataset prepared to help various LLMs learn and improve their Polish language capabilities.
38 781 sampli / samples · Apache 2.0 · Język / Language: PL (+ pary tłumaczeniowe PL↔EN)
Format
Każdy sample / every sample:
{"messages": [
{"role": "user", "content": "..."},
{"role": "assistant", "content": "..."}
]}
Struktura / Structure… See the full description on the dataset page: https://huggingface.co/datasets/JohnTdi/polish-llm-sft-pl.agentic-llm-pretraining-1.7b
Agentic LLM Pretraining Dataset
A pretraining corpus for small language models (1-3B parameters) optimized for agentic tasks. The corpus emphasizes learning to comprehend language, reason, follow instructions, and use tools over memorizing factual knowledge — the assumption is that domain knowledge will be provided at runtime via RAG. The idea is that this could enable much smaller pretraining corpora by omitting the large volumes of text typically needed to memorize facts.… See the full description on the dataset page: https://huggingface.co/datasets/visionscaper/agentic-llm-pretraining-1.7b.bigbenchhard-mt-pt
BBH-PT (Big-Bench Hard)
Portuguese machine translation of BIG-Bench Hard, a challenging subset of the BIG-Bench benchmark covering diverse reasoning tasks.
Translated using a Finetuned GemmaX2-9B for pt-PT with rule-based adaptations.
Note: Some tasks (e.g., hyperbaton) are not translated as they do not transfer meaningfully to Portuguese.
Original Dataset: https://github.com/suzgunmirac/BIG-Bench-Hard
Note: This dataset is machine translated and may contain… See the full description on the dataset page: https://huggingface.co/datasets/amalia-llm/bigbenchhard-mt-pt.LLM-self-identification Self Identification – Give your Language model an identity
About Self Identification
Self identification is training set SupraLabs curated for developers/trainers to experiment with to let your language model know about their identity.
Self identification let your LM know these information about them:
Model ID
Model Name
Model Description
Model Creator
Model Family
Model Architecture
Parameter Count
Knowledge Cutoff
Here is an example from the dataset:
If… See the full description on the dataset page: https://huggingface.co/datasets/SupraLabs/LLM-self-identification.pt_exams
PHEB - Portuguese High School Exams MCQ
MCQ set of PHEB a collection of Portuguese exam questions for evaluating language models on academic knowledge on the Portuguese curriculum.
For more details, see the PHEB paper.
This dataset is provided as part of the AMALIA project and is included in AMALIA-Bench, a comprehensive benchmark suite for evaluating large language models on European Portuguese.
Citation
If you use this dataset or AMALIA in your work… See the full description on the dataset page: https://huggingface.co/datasets/amalia-llm/pt_exams.llmsql-benchmark
LLMSQL Benchmark
⚠️ A newer version of this dataset is available:👉 https://huggingface.co/datasets/llmsql-bench/llmsql-2.0
This benchmark is designed to evaluate text-to-SQL models. For usage of this benchmark see https://github.com/LLMSQL/llmsql-benchmark.
Arxiv Article: https://arxiv.org/abs/2510.02350
Files
tables.jsonl — Database table metadata
questions.jsonl — All available questions
train_questions.jsonl, val_questions.jsonl, test_questions.jsonl — Data… See the full description on the dataset page: https://huggingface.co/datasets/llmsql-bench/llmsql-benchmark.LiDAR-LLM-Nu-Caption
Dataset Details
Dataset type:
This is the nu-Caption dataset, a QA dataset designed for training MLLM models on caption tasks in autonomous driving scenarios. It is built upon the NuScenes dataset.
Dataset keys:
"answer" is the output of the VLM models using image data. "answer_lidar" uses GPT4O-mini to filter information that cannot be obtained from the image data.
If you want to train the model like LiDAR-LLM, which only uses the LiDAR modality and does not use the vision modality… See the full description on the dataset page: https://huggingface.co/datasets/Senqiao/LiDAR-LLM-Nu-Caption.llm_eval_promptsalba_mcq
ALBA MCQ
Multiple Choice Version of the ALBA benchmark, a Portuguese language benchmark for proficiency in pt-PT linguistic-related tasks.
For more details, see the ALBA paper.
This dataset is provided as part of the AMALIA project and is included in AMALIA-Bench, a comprehensive benchmark suite for evaluating large language models on European Portuguese.
Citation
If you use this dataset or AMALIA in your work, please cite:… See the full description on the dataset page: https://huggingface.co/datasets/amalia-llm/alba_mcq.databricks-dolly-15k-ja
databricks-dolly-15k-ja
This repository provides an instruction tuning dataset developed by LLM-jp, a collaborative project launched in Japan.
This dataset is a Japanese translation of databricks-dolly-15k using DeepL.
Send Questions to
llm-jp(at)nii.ac.jp
Model Card Authors
The names are listed in alphabetical order.
Hirokazu Kiyomaru, Hiroshi Matsuda, Jun Suzuki, Namgi Han, Saku Sugawara, Shota Sasaki, Shuhei Kurita, Taishi Nakamura, Takashi Kodama, Takumi… See the full description on the dataset page: https://huggingface.co/datasets/llm-jp/databricks-dolly-15k-ja.hunter-llm-sft-v1
HunterLLM SFT v1
Instruction + preference dataset for training a bug-bounty / offensive-security
assistant that thinks like a red-teamer, prioritizes attacker primitives and
reachable impact, and writes triager-friendly reports — strictly scoped to
authorized engagements (bug bounty programs, contracted pentests, isolated
labs).
Files
File
Rows
Schema
sft_train.jsonl
~33.1k
{instruction, input, output, tags, meta}
dpo_pairs.jsonl
~33.1k
{prompt, chosen… See the full description on the dataset page: https://huggingface.co/datasets/jabir-khan/hunter-llm-sft-v1.llm-forecast-calibration
LLM Forecast Calibration Study — GLM-5.3 on resolved Manifold Markets questions
Raw generation data for the study "Does sampling K times beat thinking harder?
A controlled study of LLM forecast calibration on resolved binary questions."
Source repo: EzraStone/llm-forecast-calibration.
Data mirrored from GitHub commit 0f12f71a2c2ec8c54cafeb4231fecb87e705e660.
All eight JSONL files match the source data byte for byte. The source repository
remains canonical for analysis code… See the full description on the dataset page: https://huggingface.co/datasets/ezra77/llm-forecast-calibration.LiDAR-LLM-Nu-Grounding
Dataset Details
Dataset type:
This is the nu-Grounding dataset, a QA dataset designed for training MLLM models for grounding in autonomous driving scenarios. This QA dataset is built upon the NuScenes dataset.
Where to send questions or comments about the dataset:
https://github.com/Yangsenqiao/LiDAR-LLM
Project Page:
https://sites.google.com/view/lidar-llm
Paper:
https://arxiv.org/abs/2312.14074
tiny-llm-synthetic-qa
Tiny-LLM: Synthetic Question-Answering Dataset
Dataset Description
This dataset was created for the fine-tuning stage of the Tiny-LLM Project, a project focused on training and evaluating compact language models from scratch.
It contains 706,727 high-quality, synthetic multi-turn Question-Answering (Q&A) conversations in English, generated using the Gemini API. The dataset was designed to teach small models instruction-following capabilities across a diverse range of… See the full description on the dataset page: https://huggingface.co/datasets/Gabriel8/tiny-llm-synthetic-qa.enterprise-llm-inference-benchmarks-2026
🚀 Enterprise LLM Inference & Fine-Tuning Benchmarks (2026 Guide)
A curated benchmark index and architectural guide evaluating open-source foundation models, real-time inference engines (vLLM vs. TensorRT-LLM), and cloud GPU economics for enterprise deployments.
🧠 Open-Source Foundation Model Benchmarks (RAG & Code Generation)
Flagship Evaluation: Top Open-Source LLMs for Enterprise RAG & Code Generation (2026 In-Depth Guide) — Comparing Qwen 2.5 Coder, Llama… See the full description on the dataset page: https://huggingface.co/datasets/Abdulrahmankalil/enterprise-llm-inference-benchmarks-2026.TG_LLM_output
TG-LLM Output
This dataset contains inference outputs from the two-stage TG-LLM framework
on the TGQA test set. TG-LLM performs temporal reasoning in two steps:
Story-to-Temporal-Graph Translation (Story2TG): converts a story into a
temporal graph.
Temporal-Graph Reasoning (TGR): reasons over the predicted temporal graph
to answer temporal questions.
The original TGQA dataset is available at
sxiong/TGQA, and the source code
is available in the
TG-LLM repository.… See the full description on the dataset page: https://huggingface.co/datasets/sxiong/TG_LLM_output.food-science-llm-protocol
Food Science LLM Text-Mining Protocol
Pipeline and derived data accompanying:
Guo X, Fu W. Data Mining and Text Mining Using Large Language Models.
In: Li Y, Zhang D, Guo Z (eds), AI in Food Science: Methods and Protocols.
Methods and Protocols in Food Science. Springer.
The chapter prints one protocol as 26 numbered steps with abbreviated code
listings. This repository is the executable form of that protocol. Every step
has a corresponding function here, and every number in… See the full description on the dataset page: https://huggingface.co/datasets/KSU-HW-SEC/food-science-llm-protocol.LLMEval-1
LLMEval-1: Large-Scale Chinese LLM Evaluation (Phase I)
LLMEval-1 is the Phase I evaluation dataset of the LLMEval project (Fudan NLP Lab),
released alongside the paper "LLMEval: A Preliminary Study on How to Evaluate Large Language Models" (AAAI 2024).
It is a Chinese, generative-QA benchmark designed to study how large language models should be evaluated.
Paper: https://arxiv.org/abs/2312.07398
Project website: https://llmeval.com/
GitHub: https://github.com/llmeval/LLMEval-1… See the full description on the dataset page: https://huggingface.co/datasets/llmeval-fdu/LLMEval-1.llm-medical-reasoning-steps-benchmark
LLM Medical Reasoning Steps Benchmark
This dataset contains 1,170 medical reasoning benchmark questions with final answers, reference reasoning steps, and reference key points.
Dataset Files
data/all.jsonl: all 1,170 examples.
data/mcq.jsonl: 592 multiple-choice examples.
data/oeq.jsonl: 578 open-ended examples.
No model prediction outputs are included in this release.
Schema
Each JSONL row has the following fields:
{
"id": "mcq_0001",
"task_type":… See the full description on the dataset page: https://huggingface.co/datasets/medreason/llm-medical-reasoning-steps-benchmark.cleand_microsoft_rStar-Coder元データ: https://huggingface.co/datasets/microsoft/rStar-Coder
データ件数: 269,863
平均トークン数: 11674
最大トークン数: 31,184
合計トークン数: 3,150,447,484
ファイル形式: JSONL
ファイルサイズ: 不明
加工内容
synthetic_sftを使用
トークン処理が重たいので、文字数でフィルター
seed_question < 6000
generation < 80000
thinkタグ除去 が中途半端なものを除外
トークナイズ処理（速度向上アップデート
繰り返し除去
EarthScience-Text-LLM-20K-90-10
EarthScience-Text-LLM-20K-90-10
This is a pure-text Earth-science corpus unified from three non-overlapping upstream datasets:
Ekimetrics/climateqa-ipcc-ipbes-reports-1.0: climate and IPCC/IPBES report chunks.
GeoGPT-Research-Project/GeoGPT-CoT-QA: geoscience question-answer reasoning.
gremlin97/RemoteSensingCorpus: remote-sensing and geospatial machine-learning text.
Files and Split
The previous preprocessing outputs were merged into a 23,098-record pool and… See the full description on the dataset page: https://huggingface.co/datasets/moTcream/EarthScience-Text-LLM-20K-90-10.persona_nemotron
Persona Nemotron PT Datasets
This is a collection of Portuguese synthetic datasets, consisting of 3 datasets, one with general questions from varied topics, one with math questions, and one with instruction-following requests.
The prompts were generated using an approach similar to PersonaHub, with a translated version of Nemotron Personas. Both prompts and answers were generated using Gemma 3-27B.
This dataset is provided as part of the AMALIA project and is… See the full description on the dataset page: https://huggingface.co/datasets/amalia-llm/persona_nemotron.AMALIA-LLM-0626-SFT-Dataset
AMALIA LLM Supervised Finetuning Dataset
Data mix used in the Supervised Finetuning stage of the post-training of the AMALIA model. This data mix includes both the mixes used in the base and ramp down phases of the SFT training.
Base Data Mix
This data mix contains off-the-shelf datasets and developed by the AMALIA team. The dataset counts are described in the following table:
Dataset
Count
amalia-llm/persona_math
63,731… See the full description on the dataset page: https://huggingface.co/datasets/amalia-llm/AMALIA-LLM-0626-SFT-Dataset.pyraFiltered dataset sourced from https://huggingface.co/datasets/bnadimi/PyraNet-Verilog for SFT. Keep only high-quality data. Check https://github.com/CatIIIIIIII/VeriPrefer for usage.
LLMEval-Logic
LLMEval-Logic — Public 80% Release
A Solver-Verified Chinese Benchmark for Logical Reasoning of LLMs with Adversarial Hardening.
📄 Paper (arXiv): https://arxiv.org/abs/2605.19597
🌐 Project: https://llmeval.com/
🐙 Code & evaluation pipeline: https://github.com/llmeval/LLMEval-Logic
🤗 Dataset (this card): https://huggingface.co/datasets/llmeval-fdu/LLMEval-Logic
⚠️ This is the 80% public release
LLMEval-Logic was built through a three-stage audit pipeline: (a)… See the full description on the dataset page: https://huggingface.co/datasets/llmeval-fdu/LLMEval-Logic.