datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
tulu-3-sft-personas-instruction-following
Dataset Descriptions
This dataset contains 29980 examples and is synthetically created to enhance model's capabilities to follow instructions precisely and to satisfy user constraints. The constraints are borrowed from the taxonomy in IFEval dataset.
To generate diverse instructions, we expand the methodology in Ge et al., 2024 by using personas. More details and exact prompts used to construct the dataset can be found in our paper.
Curated by: Allen Institute for AI
Paper: TBD… See the full description on the dataset page: https://huggingface.co/datasets/allenai/tulu-3-sft-personas-instruction-following.Nemotron-SFT-Instruction-Following-Chat-v3
Dataset Description:
The Nemotron-Instruction-Following-Chat-v3 dataset is designed to strengthen multi-turn, interactive capabilities, including open-ended chat and precise instruction following.
The chat subset uses human written prompts from sources like lmarena, lmsys, and wildchat as seed prompts. Responses are generated with GLM-5. Multiple responses are sampled from the model and the best response as judged by pairwise comparisons using Qwen3-Nemotron-235B-A22B-GenRM-2603… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Instruction-Following-Chat-v3.Nemotron-SFT-Instruction-Following-Chat-v2
Dataset Description:
The Nemotron-Instruction-Following-Chat-v2 dataset is designed to broadly strengthen the model’s interactive capabilities, including open-ended chat and precise instruction following.The dataset is a refreshed version of Nemotron-Instruction-Following-Chat-v1 with synthetic dialogues generated from Kimi-K2-Thinking, GLM-4.6, Qwen3-235B-A22B-Thinking-2507, GPT-OSS-120b, Kimi-K2-Instruct-0905, and Qwen3-235B-A22B-Instruct-2507.
This dataset is ready for commercial… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Instruction-Following-Chat-v2.Instruction-Following-IFEval
SEA-IFEval
SEA-IFEval evaluates a model's ability to adhere to constraints provided in the prompt, for example beginning a response with a specific word/phrase or answering with a certain number of sections. It is based on IFEval and was manually translated by native speakers for Indonesian, Javanese, Sundanese, Thai, Tagalog, and Vietnamese.
Supported Tasks and Leaderboards
SEA-IFEval is designed for evaluating chat or instruction-tuned large language models (LLMs).… See the full description on the dataset page: https://huggingface.co/datasets/aisingapore/Instruction-Following-IFEval.Nemotron-RL-Instruction-Following-Structured-Outputs-v2
Dataset Description:
Split 1: Direct Generation tests the model’s ability to perform freeform text structured outputs on JSON, YAML, and XML data, varying the complexity and presentation of the schema.
Split 2: Diversified Tasks adds 2 additional output formats: TOML and CSV, while increasing problem types to Direct Extraction from document, Translation between formats, Multistep Translation from known data, Multistep Extraction from unrelated context, Schema-Only Generation for… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Instruction-Following-Structured-Outputs-v2.Nemotron-RL-Instruction-Following-Free-Form-Formatting-v1
Dataset Description:
Teaches the model to follow arbitrary text formatting instructions (bullet styles, numbering, delimiters, heading formats, inline emphasis, web-answer structure, etc.) for targeted chat behaviors. Uses explicit Regex and string matching for the reward signal.
This dataset is ready for commercial or non-commercial uses.
Dataset Owner(s):
NVIDIA Corporation
Dataset Creation Date:
Created on: April 10, 2026
Last Modified on: April… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Instruction-Following-Free-Form-Formatting-v1.llm-instruction-following-eval
LLM Instruction-Following Evaluation: 256 Models Across 20 Diagnostic Tests
Dataset Summary
This dataset contains comprehensive evaluation results from testing 256 Large Language Models across 20 carefully designed diagnostic instruction-following prompts, totaling 5,120 individual evaluations. The evaluation was conducted on October 14, 2025, using the OpenRouter API.
Paper: When Models Can't Follow: Testing Instruction Adherence Across 256 LLMs
arXiv: 2510.18892… See the full description on the dataset page: https://huggingface.co/datasets/richardyoung/llm-instruction-following-eval.Nemotron-RL-Instruction-Following-Citation-Formatting-v1
Dataset Description:
Teaches the model to cite specific document parts using reference markers like [ref:1], ref:3, etc. Supports single-reference, multi-reference, and inline citations.
This dataset is ready for commercial/non-commercial uses.
Dataset Owner(s):
NVIDIA Corporation
Dataset Creation Date:
Created on: April 10, 2026
Last Modified on: April 10, 2026
Version:
Nemotron-RL-Instruction-Following-CitationFormatting-v1… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Instruction-Following-Citation-Formatting-v1.instruction-following-hard-sft-100k
Hard Instruction Following SFT (100K)
100,000 ShareGPT conversations where the assistant correctly satisfies multiple simultaneous explicit constraints in a single response. Each example pairs a multi-constraint prompt with a response that honors every constraint without dropping any.
Targets the instruction-following capability measured by IFEval and similar benchmarks.
Motivation
A key failure mode in deployed LLMs is dropping constraints under load — responding… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/instruction-following-hard-sft-100k.RL-Instruction-Following-Dataset
RL-Instruction-Following-Dataset 🎯
A Verifiable, Rule-Based Dataset for Reinforcement Learning with Verifiable Rewards (RLVR)
📖 Dataset Card 🚀 Usage ⚖️ License
Overview
This dataset is designed to enhance the Instruction Following capabilities of Large Language Models (LLMs) through Reinforcement Learning (RL). Unlike subjective preference datasets (e.g., standard RLHF), this dataset focuses on Objective, Rule-Based Constraints.
Each entry provides a prompt with… See the full description on the dataset page: https://huggingface.co/datasets/renhuimin/RL-Instruction-Following-Dataset.instruction-following-rl-66k
Instruction Following RL 66K
Dataset overview
instruction-following-rl-66k is an English training dataset for instruction-following reinforcement learning (RL/RLVR), containing 66,418 examples. It is derived primarily from AllenAI's IF_multi_constraints_upto5, whose instructions contain up to five verifiable constraints drawn from IFEval and IFBench-Train.
Each record is first validated for its JSON, prompt, and metadata structure. A predefined… See the full description on the dataset page: https://huggingface.co/datasets/wflying/instruction-following-rl-66k.instruction-following-training-pool
Instruction following training pool
Public prompts for writing tasks, many of them carrying a constraint a program can check, from
eight datasets read at the pinned revisions named below and one layer built here from them. The
pool is laid out twice. Train on either layer or on both.
pool.jsonl
Every source rewritten into one shape, 310602 rows, one JSON object per line, with these fields.
Field
What it holds
id
a row identifier unique within this file… See the full description on the dataset page: https://huggingface.co/datasets/Emulated-Inc/instruction-following-training-pool.eg-legal-instruction-following
Arabic Legal Dataset - Legal Instruction Following
Dataset Description
Instruction-following dataset for diverse legal text analysis tasks through natural language commands.
This dataset contains 4,184 examples of instruction_following data derived from Egyptian legal texts, including criminal law, civil law, procedural law, and personal status law. The dataset is designed for training and evaluating Arabic legal AI models.
Dataset Summary
Language: Arabic… See the full description on the dataset page: https://huggingface.co/datasets/fr3on/eg-legal-instruction-following.swe-coding-instruction-following
SWE Coding Instruction-Following
A curated collection of real-world software engineering tasks in the SWE-bench format, designed for evaluating instruction-following capabilities of coding agents. Each task represents a genuine GitHub issue with a reproducible environment, test suite, and reference solution — the agent must precisely follow the issue instructions to produce a correct fix.
Overview
Item
Details
Total Tasks
50
Repositories
2 (pallets/click… See the full description on the dataset page: https://huggingface.co/datasets/obaydata/swe-coding-instruction-following.nvidia_instruction_following_if_split_v3
Dataset Description
This is the instruction_following split only (the chat split was intentionally excluded) from
nvidia/Nemotron-SFT-Instruction-Following-Chat-v3,
re-packaged as Parquet (sharded) instead of the original single JSONL file for faster loading and native
support in the HF datasets viewer.
No content was modified — this is a straight format conversion of the instruction_following subset.
Source dataset: nvidia/Nemotron-SFT-Instruction-Following-Chat-v3
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/tuandunghcmut/nvidia_instruction_following_if_split_v3.instruction-following-rl-content-constrained-30k
Instruction-Following RL Content-Constrained 30K
Dataset summary
Instruction-Following RL Content-Constrained 30K is a training dataset for precise instruction following and reinforcement learning from verifiable rewards (RLVR). The current cleaned revision contains 29,520 heterogeneous, single-turn user prompts. Each prompt combines a substantive task with one to five explicit output constraints, such as keyword inclusion or exclusion, response length… See the full description on the dataset page: https://huggingface.co/datasets/wflying/instruction-following-rl-content-constrained-30k.instruction-following-dpo-100k
Instruction Following DPO (100K)
100,000 DPO preference pairs training LLMs to follow explicit formatting and structural constraints exactly — word counts, list lengths, output formats, tone, language, and more.
Motivation
Format non-compliance is one of the most common and costly LLM failure modes in production:
Model gives 6 bullet points when asked for exactly 5
Returns markdown-wrapped JSON when raw JSON was required
Ignores word limits, producing… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/instruction-following-dpo-100k.nvidia_instruction_following_if_split_v3_non_thinking
Dataset Description
Non-thinking (no chain-of-thought) variant of
tuandunghcmut/nvidia_instruction_following_if_split_v3,
which is itself the instruction_following split of
nvidia/Nemotron-SFT-Instruction-Following-Chat-v3.
The reasoning_content field has been fully removed from every message (not just nulled) — each message
now only has role and content. This is intended for training/evaluation setups that do not use
chain-of-thought / reasoning traces.
Source dataset:… See the full description on the dataset page: https://huggingface.co/datasets/tuandunghcmut/nvidia_instruction_following_if_split_v3_non_thinking.Nemotron-SFT-Instruction-Following-Chat-v2
Dataset Description:
The Nemotron-Instruction-Following-Chat-v2 dataset is designed to broadly strengthen the model’s interactive capabilities, including open-ended chat and precise instruction following.The dataset is a refreshed version of Nemotron-Instruction-Following-Chat-v1 with synthetic dialogues generated from Kimi-K2-Thinking, GLM-4.6, Qwen3-235B-A22B-Thinking-2507, GPT-OSS-120b, Kimi-K2-Instruct-0905, and Qwen3-235B-A22B-Instruct-2507.
This dataset is ready for… See the full description on the dataset page: https://huggingface.co/datasets/OperatorSDG/Nemotron-SFT-Instruction-Following-Chat-v2.stratified-kmeans-diverse-instruction-following-100K-1M
Stratified K-Means Diverse Instruction-Following Dataset (100K-1M)
A carefully balanced subset combining Tulu-3 SFT Mixture and Orca AgentInstruct, featuring embedding-based k-means sampling across diverse instruction-following tasks at multiple scales.
👥 Follow the Authors
Aman Priyanshu
Supriti Vijay
Overview
This dataset provides stratified subsets at 50k, 100k, 250k, 500k, and 1M scales, combining high-quality instruction-following data from… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/stratified-kmeans-diverse-instruction-following-100K-1M.persona_instruction_following
Persona Instruction Following Datasets
This is a synthetic instruction-following dataset, available in two configs: full and filtered. Each config contains two language splits, English (en) and Portuguese (pt). The filtered version keeps only the higher-quality examples (quality score 5).
The prompts were generated using an approach similar to PersonaHub, with a translated version of proj-persona/PersonaHub. Both prompts and answers were generated using Gemma 3-27B.… See the full description on the dataset page: https://huggingface.co/datasets/amalia-llm/persona_instruction_following.reasoning-sft-Nemotron-Instruction-Following-Chat-v1
Nemotron Instruction Following Chat v1 (Reasoning SFT)
Converted version of nvidia/Nemotron-Instruction-Following-Chat-v1, filtered to 157,595 rows where assistant responses include genuine reasoning traces (reasoning_content).
Format
Each row has three columns:
input — list of dicts with role/content conversation turns (system, user, and prior assistant turns up to the final assistant response)
response — <think> block containing the model's reasoning followed by the… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/reasoning-sft-Nemotron-Instruction-Following-Chat-v1.tulu-3-sft-personas-instruction-following-es
Tulu 3 SFT Personas Instruction Following (Spanish Translation)
This dataset is a Spanish translation of allenai/tulu-3-sft-personas-instruction-following, a 30k-example supervised fine-tuning dataset designed to improve instruction following and constraint satisfaction in chat models.
The translation was created to make this style of instruction-following data more useful for Spanish-language model development while keeping the original task structure intact.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/fabriciocarraro/tulu-3-sft-personas-instruction-following-es.Nemotron-RL-Instruction-Following-Citation-Formatting-v1
Dataset Description:
Teaches the model to cite specific document parts using reference markers like [ref:1], ref:3, etc. Supports single-reference, multi-reference, and inline citations.
This dataset is ready for commercial/non-commercial uses.
Dataset Owner(s):
NVIDIA Corporation
Dataset Creation Date:
Created on: April 10, 2026
Last Modified on: April 10, 2026
Version:
Nemotron-RL-Instruction-Following-CitationFormatting-v1… See the full description on the dataset page: https://huggingface.co/datasets/Arsh9210/Nemotron-RL-Instruction-Following-Citation-Formatting-v1.Nemotron-RL-Instruction-Following-Free-Form-Formatting-v1
Dataset Description:
Teaches the model to follow arbitrary text formatting instructions (bullet styles, numbering, delimiters, heading formats, inline emphasis, web-answer structure, etc.) for targeted chat behaviors. Uses explicit Regex and string matching for the reward signal.
This dataset is ready for commercial or non-commercial uses.
Dataset Owner(s):
NVIDIA Corporation
Dataset Creation Date:
Created on: April 10, 2026
Last Modified on: April… See the full description on the dataset page: https://huggingface.co/datasets/Arsh9210/Nemotron-RL-Instruction-Following-Free-Form-Formatting-v1.amalia-Nemotron-SFT-Instruction-Following-Chat-v2
Nemotron-SFT-Instruction-Following-Chat-v2
Version of the nvidia/Nemotron-SFT-Instruction-Following-Chat-v2 dataset used in the AMALIA's Supervised Fine-Tuning stage, both in the base and ramp down stages. The ramp down stage comprised a randomly select subset of the base subset, where part was translated to European Portuguese using google/gemma-4-31B-it.
This dataset went through a processing pipeline to:
Remove entries that reference other LLMs or research labs;… See the full description on the dataset page: https://huggingface.co/datasets/amalia-llm/amalia-Nemotron-SFT-Instruction-Following-Chat-v2.Nemotron-RL-Instruction-Following-Structured-Outputs-v2
Dataset Description:
Split 1: Direct Generation tests the model’s ability to perform freeform text structured outputs on JSON, YAML, and XML data, varying the complexity and presentation of the schema.
Split 2: Diversified Tasks adds 2 additional output formats: TOML and CSV, while increasing problem types to Direct Extraction from document, Translation between formats, Multistep Translation from known data, Multistep Extraction from unrelated context, Schema-Only Generation for… See the full description on the dataset page: https://huggingface.co/datasets/Arsh9210/Nemotron-RL-Instruction-Following-Structured-Outputs-v2.io_concise_instructionfollowing
Concise Instruction Following Dataset
Overview
This dataset is a concise version of the original allenai/tulu-3-sft-personas-instruction-following dataset, processed using meta-llama/Llama-3.3-70B-Instruct to create more concise yet comprehensive responses while maintaining all original constraints and quality.
Dataset Creation
Source Dataset
Original: allenai/tulu-3-sft-personas-instruction-following
Original Size: 29,980 examples
Purpose: Enhanced… See the full description on the dataset page: https://huggingface.co/datasets/io-net/io_concise_instructionfollowing.rosetta-ko-instruction-following-synth-rlvr
rosetta-ko-instruction-following-synth-rlvr
Korean-native instruction-following data — instructions with verifiable constraints (length, format, keywords, JSON, ...) checked by programmatic verifiers.
Synthetic data generated with the Qwen3.6-27B teacher model — part of the Rosetta-KO suite for the Rosetta Korean LLM (PoSTMEDIA).
Instruction-Following Suite
Sibling datasets from the same pipeline (each a separate repo):
repo
format… See the full description on the dataset page: https://huggingface.co/datasets/PoSTMEDIA/rosetta-ko-instruction-following-synth-rlvr.rosetta-ko-instruction-following-synth-sft-think
rosetta-ko-instruction-following-synth-sft-think
Korean-native instruction-following data — instructions with verifiable constraints (length, format, keywords, JSON, ...) checked by programmatic verifiers.
Synthetic data generated with the Qwen3.6-27B teacher model — part of the Rosetta-KO suite for the Rosetta Korean LLM (PoSTMEDIA).
Instruction-Following Suite
Sibling datasets from the same pipeline (each a separate repo):
repo
format… See the full description on the dataset page: https://huggingface.co/datasets/PoSTMEDIA/rosetta-ko-instruction-following-synth-sft-think.