datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Nexus-Agents-ToolCalling
Nexus Agents — Tool-Calling Conversations
Synthetic, schema-verified tool-calling conversations for training the Nexus Projects
agents. This is the exact data behind
Nemotron-3-Nano-30B-A3B — Nexus Agents (GGUF),
including the verification transcripts that scored it (27/27 on the behavioral
interview eval, vs 13/27 for the base model).
Links: the fine-tuned model →
Nemotron-3-Nano-30B-A3B — Nexus Agents (GGUF) ·
the generator + seed data + eval harness →
Nexus Training Studio ·… See the full description on the dataset page: https://huggingface.co/datasets/NexusProjectsAI/Nexus-Agents-ToolCalling.NExtLong-512K-dataset
NExtLong: Toward Effective Long-Context Training without Long Documents
This repository contains the code ,models and datasets for our paper NExtLong: Toward Effective Long-Context Training without Long Documents.
[Github]
Quick Links
Overview
NExtLong Models
NExtLong Datasets
Datasets list
How to use NExtLong datasets
Bugs or Questions?
Overview
Large language models (LLMs) with extended context windows have made significant strides yet remain a… See the full description on the dataset page: https://huggingface.co/datasets/caskcsg/NExtLong-512K-dataset.NExtLong-64K-dataset
NExtLong: Toward Effective Long-Context Training without Long Documents
This repository contains the code ,models and datasets for our paper NExtLong: Toward Effective Long-Context Training without Long Documents.
[Github]
Quick Links
Overview
NExtLong Models
NExtLong Datasets
Datasets list
How to use NExtLong datasets
Bugs or Questions?
Overview
Large language models (LLMs) with extended context windows have made significant strides yet remain a… See the full description on the dataset page: https://huggingface.co/datasets/caskcsg/NExtLong-64K-dataset.NExtLong-128K-dataset
NExtLong: Toward Effective Long-Context Training without Long Documents
This repository contains the code ,models and datasets for our paper NExtLong: Toward Effective Long-Context Training without Long Documents.
[Github]
Quick Links
Overview
NExtLong Models
NExtLong Datasets
Datasets list
How to use NExtLong datasets
Bugs or Questions?
Overview
Large language models (LLMs) with extended context windows have made significant strides yet remain a… See the full description on the dataset page: https://huggingface.co/datasets/caskcsg/NExtLong-128K-dataset.SWE-Next
SWE-Next: Scalable Real-World Software Engineering Tasks for Agents
SWE-Next Dataset
SWE-Next is an execution-grounded dataset of 2,308 self-verifying software engineering tasks mined from real merged GitHub pull requests. Starting from 3,971 seeded Python repositories and 102,582 executed candidate base/merged commit pairs, SWE-Next retains only instances where the merged commit produces a strict test improvement without regressions. The final release… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/SWE-Next.NextNightSWE-Next-SFT-Trajectories
SWE-Next: Scalable Real-World Software Engineering Tasks for Agents
SWE-Next SFT Trajectories
SWE-Next SFT Trajectories is the supervised fine-tuning dataset released with SWE-Next: Scalable Real-World Software Engineering Tasks for Agents. It contains 3,693 ShareGPT-style multi-turn training examples collected from expert agent rollouts on 2,308 execution-grounded SWE tasks synthesized from real merged pull requests.
The dataset is designed for training… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/SWE-Next-SFT-Trajectories.fable5-agentic-coding-sft
FABLE.5 Agentic Coding SFT (curated)
~159,972 supervised fine-tuning examples for agentic coding — multi-turn conversations where the
assistant drives a tool-call loop (shell, file edits, tests) and commits to complete solutions. Used to train
VibeThinker-Fable-Nano-Agentic-3B.
Provenance & license
Curated/distilled from the Complete-FABLE.5-traces-2M trace set:
Original source: Glint-Research/Complete-FABLE.5-traces-2M (currently gated).
Pulled from:… See the full description on the dataset page: https://huggingface.co/datasets/Nexlab/fable5-agentic-coding-sft.Ultrachat-Sharegpt-Qwen3-Next-80B-A3B-InstructNextSearch-1-Trajectories
NextSearch-1 Trajectories
The supervised training trajectories behind the
NextSearch-1 web research agents:
complete research episodes — reasoning, tool calls, live-web tool results,
and final answers — for every task in the companion
NextSearch-1-Tasks
SFT configs. Directly trainable: each row is a prompt (messages) plus a
target trajectory (target) with per-message reasoning and OpenAI-format
tool calls.
Technical report: nexttoken.co/research/nextsearch-1 ·
Harness and evals:… See the full description on the dataset page: https://huggingface.co/datasets/NextTokenAI/NextSearch-1-Trajectories.NExT-QASee https://github.com/doc-doc/NExT-QA for more details. Download video from google drive.
only contain multi-choice questions.
Statistics:
Train
Val
Test
Total
34132
4996
8564
47692
ATP-hard subset could be found in https://github.com/Hritikbansal/videocon, see datasets/nextqa-atphard.csv.
glm5-next-tiny-fidelity-root-v1
glm5_next random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/glm5-next-tiny-random-bf16.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it). Same… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm5-next-tiny-fidelity-root-v1.next.js-15.4-with-reasoning
Description
The Next.js Documentation Dataset based on next.js 15.4 version is a high-quality, code-centric dataset created from Next.js documentation for fine-tuning language models. It contains 1,172 question-answer pairs derived from 178 markdown documentation files, focusing on practical code examples and real-world development scenarios.
This dataset is designed for:
Question Answering: Natural language questions about Next.js development
Code Generation: Generating practical… See the full description on the dataset page: https://huggingface.co/datasets/Slava32/next.js-15.4-with-reasoning.clean-PD-16000-books3
📚 clean-PD-16000-books3
A treasure trove of ~16,000 high-quality, public domain books in English language — nicely cleaned, with rich metadata, and ready for language modeling.
✨ What Makes This Dataset Special?
This isn’t just another dump of dusty old text files.
clean-PD-16000-books3 is the result of a rigorous cleaning and curation process applied to a large collection of public domain literature, including:
✅ Readable prose — paragraphized prose, without unnatural… See the full description on the dataset page: https://huggingface.co/datasets/next-token/clean-PD-16000-books3.socraticDataset1680
Socratic AI Pedagogy Preference Dataset (1,680 Quadruplets)
A curated multi-domain educational preference dataset for post-training open language models into pedagogical Socratic tutors for introductory Artificial Intelligence and Machine Learning courses.
📚 Dataset Overview
The dataset contains 1,680 paired preference quadruplets across five foundational AI subfields:
Classical Search & Planning (347 items): A* heuristic admissibility, graph search state-space… See the full description on the dataset page: https://huggingface.co/datasets/NextGenInstitute/socraticDataset1680.nextjs-14NextSearch-1-Tasks
NextSearch-1 Tasks
The task pools behind the NextSearch-1
web research agents: every row is a research question with its reference
answer and grading spec — the sft-tasks configs are the tasks behind the
supervised corpora, the rl-tasks configs the verified prompt+gold pools
used for reinforcement learning. Full trajectories for the SFT configs are in the companion
NextSearch-1-Trajectories.
Technical report: nexttoken.co/research/nextsearch-1 ·
Harness and evals:… See the full description on the dataset page: https://huggingface.co/datasets/NextTokenAI/NextSearch-1-Tasks.Agent-Trajectory-Dataset
Description
본 데이터셋은 심층 검색, 데이터 분석, 산업 리서치 등 사무 환경에서 수행되는 다양한 작업 시나리오를 포함하며, 완전한 멀티턴 추론 과정과 도구 호출 체인으로 구성되어 있습니다. 에이전트의 계획 수립 능력 분석, 도구 선택 전략 연구 및 작업 품질 평가를 지원하도록 설계되었으며, 에이전트 학습 및 평가를 위한 구조화된 벤치마크로 활용할 수 있습니다.
자세한 내용은 아래 링크를 참고해 주세요: https://ko.nexdata.ai/datasets/llm/2185?source=hf.kr
Specifications
Data content
OpenClaw를 통해 생성된 에이전트 트래젝토리 데이터
Category
심층 검색, 데이터 분석, 산업 리서치
Data volume
5,300
Model… See the full description on the dataset page: https://huggingface.co/datasets/Nexdata-kr/Agent-Trajectory-Dataset.Litelong_Nextlong_128kAgent-Trajectory-Data-Sample
Agent-Trajectory-Dataset
Description
This dataset covers office-based scenarios such as in-depth searches, data analysis, and industry research, encompassing complete multi-turn reasoning trajectories and tool-calling chains. It is designed to support the analysis of agent planning capabilities, research into tool selection strategies, and quality assessment, providing a structured benchmark for agent training and evaluation.
For more details, please refer to the… See the full description on the dataset page: https://huggingface.co/datasets/Nexdata-AI/Agent-Trajectory-Data-Sample.MVEmo
MVEmo
This is the dataset repository for the paper: Bridging Categorical and Dimensional Affect: The MVEmo Multi-Task Benchmark for Music-Related Emotion Recognition.
Dataset Details
Dataset Description
MVEmo is a large-scale multimodal dataset that consists of 11,764 music video samples with both static and dynamic emotion annotations for music-related emotion recognition (MRER). It consists of the following key features:
Basic Information: title, artist… See the full description on the dataset page: https://huggingface.co/datasets/NEXTLab-ZJU/MVEmo.Nextec-Code-Claude
Nextec-Code-Claude
Dataset Summary
Nextec-Code-Claude is a dataset of coding-focused conversational examples designed for training and evaluating code generation, software engineering assistants, and agentic coding systems. The dataset contains interactions that emphasize programming tasks, debugging, code explanation, refactoring, and software development workflows.
The dataset is intended for supervised fine-tuning (SFT), instruction tuning, research, and… See the full description on the dataset page: https://huggingface.co/datasets/abouelgoud/Nextec-Code-Claude.NexaFlow-SFT-DatasetLitelong_Nextlong_64knexus-sft-v1-sources
nexus-sft-v1-sources
AETHER family SFT dataset — group nexus_core.
Format: JSONL ChatML messages, task_type tagged, MinHash dedup applied (threshold 0.85).
Schema:
{
"messages": [{"role": "system|user|assistant", "content": "..."}],
"task_type": "function_calling|code|reasoning_cot|...",
"source_ds": "<HF dataset_id>",
"lang": "en|fr|...",
"system_source": "archon_default|overridden_from_source"
}
Generated by prepare_sft.py pipeline (2026-05-25).
FinCDM-Fin-KQA
FinCDM-FinEval-KQA
Repository: NextGenWhu/FinCDM-CPA-KQA
📖 Overview
FinCDM-CPA-KQA is a specialized dataset for financial knowledge-based question answering, derived from the research presented in:
"From Scores to Skills: A Cognitive Diagnosis Framework for Evaluating Financial Large Language Models"
This dataset is designed to evaluate large language models (LLMs) on their ability to perform financial knowledge reasoning, compliance assessment, and knowledge-based… See the full description on the dataset page: https://huggingface.co/datasets/NextGenWhu/FinCDM-Fin-KQA.NexusBench-trajectories
AgentSuite/NexusBench-trajectories
Per-model agent trajectory data for NexusBench (public release).
Models: 30
Tasks per model: 2,217
One file per model: {model}.jsonl, one JSON object per line.
Fields: model_path, user_model_path, benchmark_name, task_name, sampling_params, user_sampling_params, messages, eval_result, meta.
sampling_params reflect each benchmark's own implementation; values the benchmark leaves unset are recorded as null (provider default).
Models… See the full description on the dataset page: https://huggingface.co/datasets/AgentSuite/NexusBench-trajectories.NexaFlow-DPO-DatasetNexaFlow-CPT-Datasetnexus-sft-v1-mix
nexus-sft-v1-mix
AETHER family SFT dataset — group mix.
Format: JSONL ChatML messages, task_type tagged, MinHash dedup applied (threshold 0.85).
Schema:
{
"messages": [{"role": "system|user|assistant", "content": "..."}],
"task_type": "function_calling|code|reasoning_cot|...",
"source_ds": "<HF dataset_id>",
"lang": "en|fr|...",
"system_source": "archon_default|overridden_from_source"
}
Generated by prepare_sft.py pipeline (2026-05-25).