datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.instruction-following-evalNemotron-Instruction-Following-Chat-v1
Dataset Description:
The Nemotron-Instruction-Following-Chat-v1 dataset is designed to broadly strengthen the model’s interactive capabilities, spanning open-ended chat, precise instruction following, and reliable structured output generation. It combines refreshed chat data from Nemotron-Post-Training-Dataset-v2 (extended to multi-turn) with synthetic dialogues produced by strong frontier models such as GPT-OSS-120B and Qwen3-235B variants.
This dataset is ready for commercial… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Instruction-Following-Chat-v1.Nemotron-RL-instruction_following-structured_outputs
Dataset Description:
The Nemotron-RL-instruction_following-structured_outputs dataset tests the ability of the model to follow output formatting instructions under schema constraints under the JSON format. Each problem consists of three components: The document, output formatting Instruction (Schema), and question. The dataset varies the difficulty of each problem by varying the location of instructions, the comprehensiveness of instructions, the complexity of the schema, and… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-instruction_following-structured_outputs.Nemotron-RL-Instruction-Following-MultiTurnChat-v1
Dataset Description:
The MultiChallenge Dataset is a rigorous benchmark designed to improve large language models in complex multi-turn conversations by explicitly targeting inference memory, instruction retention, version editing, and self-coherence. It employs a unique "model breaking" methodology where tasks are tested against advanced models (Nemotron-Nano-V2 and Qwen3-235B-A22B-Thinking-2507) to expose failure modes. A sample is only accepted into the dataset if the task is… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Instruction-Following-MultiTurnChat-v1.Nemotron-RL-Instruction-Following-Calendar-v2
Dataset Description:
The Calendar-Scheduling-Dataset is a multi-turn conversation dataset that can understand natural language scheduling constraints, follow instructions across multiple messages, infer scheduling conflicts and satisfy multiple constraints simultaneously. Each event has constraints around duration (e.g. 45 min) and timing (e.g. should be scheduled after 3pm). The user mentions the events and associated constraints in a random order in a natural conversational… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Instruction-Following-Calendar-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.Nemotron-RL-Instruction-Following-Adversarial-v1
Dataset Description:
The inverseIF dataset focuses on adversarial prompts designed to explicitly conflict with an AI model’s standard training instincts—such as writing code without comments or refusing standard helpfulness norms—across 8 distinct "anti-convention" patterns. Using a targeted "model breaking" methodology, it generates four candidate responses via Nemotron-Nano-V2 or Qwen3-235B-A22B-Thinking-2507 to test if the negative constraint is difficult enough to force a… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Instruction-Following-Adversarial-v1.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.multi-image-composition-instruction-following
Multi-Image Composition Instruction-Following
A large-scale multimodal dataset for multi-image composition via natural language instruction-following. Each case provides 2-3 input images (characters + scene) along with detailed Chinese instructions to compose them into a single photorealistic output image.
Designed for training and evaluating models on complex image composition tasks that require understanding of character identity preservation, pose generation, scene integration… See the full description on the dataset page: https://huggingface.co/datasets/obaydata/multi-image-composition-instruction-following.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.instruction-following-eval
IFEval - Instruction Following Evaluation Dataset
This dataset is designed for evaluating how well language models follow specific instructions when generating responses. It serves as the default evaluation data for IFEval repository.
Dataset Description
The IFEval dataset contains prompts with specific instructions designed to test language models' ability to follow directions precisely. It is intended for use with the evaluation framework described in the paper… See the full description on the dataset page: https://huggingface.co/datasets/kaleinaNyan/instruction-following-eval.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.EvalAnything-InstructionFollowing
All-modality Generation (Instruction Following Part)
All-Modality Generation benchmark evaluates a model's ability to follow instructions, automatically select appropriate modalities, and create synergistic outputs across different modalities (text, visual, audio) while avoiding redundancy.
🏠 Homepage | 👍 Our Official Code Repo
🤗 All-Modality Understanding Benchmark
🤗 All-Modality Generation Benchmark (Instruction Following Part)
🤗 All-Modality Generation Benchmark (Modality… See the full description on the dataset page: https://huggingface.co/datasets/PKU-Alignment/EvalAnything-InstructionFollowing.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.Tulu-3-Personas-Instruction-Following-Filtered
Tulu 3 Personas Instruction Following Filtered
This dataset is a filtered version of AllenAI's Tulu 3 Sft Personas Instruction Following without refusals.
All refusals have been removed from this dataset, making it more efficient and effective to train on.
Sizes
Originl Dataset: 29,980 Lines
Filtered Dataset: 20,829 Lines
Format (per row)
{
"id": "",
"prompt": "",
"messages": [
{
"content": ",
"role": "user"
},
{
"content":… See the full description on the dataset page: https://huggingface.co/datasets/LucidityAI/Tulu-3-Personas-Instruction-Following-Filtered.Nemotron-RL-Instruction-Following-Structured-Outputs-v2-preferencepersona_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.amalia-Nemotron-Instruction-Following-Chat-v1
AMALIA Nemotron-Instruction-Following-Chat-v1
Version of the nvidia/Nemotron-Instruction-Following-Chat-v1 dataset used in the AMALIA's Supervised Fine-Tuning stage.
This dataset went through a processing pipeline to:
Remove entries where the capability_target field was 'chat' from the chat_if split;
Remove entries that reference other LLMs or research labs;
Remove entries that contained the string '/imagine prompt:';
Removed the reasoning_content field;
Original… See the full description on the dataset page: https://huggingface.co/datasets/amalia-llm/amalia-Nemotron-Instruction-Following-Chat-v1.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-Calendar-v2
Dataset Description:
The Calendar-Scheduling-Dataset is a multi-turn conversation dataset that can understand natural language scheduling constraints, follow instructions across multiple messages, infer scheduling conflicts and satisfy multiple constraints simultaneously. Each event has constraints around duration (e.g. 45 min) and timing (e.g. should be scheduled after 3pm). The user mentions the events and associated constraints in a random order in a natural conversational… See the full description on the dataset page: https://huggingface.co/datasets/Arsh9210/Nemotron-RL-Instruction-Following-Calendar-v2.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.rosetta-ko-instruction-following-synth-sft
rosetta-ko-instruction-following-synth-sft
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.Nemotron-Instruction-Following-Chat-v1
Dataset Description:
The Nemotron-Instruction-Following-Chat-v1 dataset is designed to broadly strengthen the model’s interactive capabilities, spanning open-ended chat, precise instruction following, and reliable structured output generation. It combines refreshed chat data from Nemotron-Post-Training-Dataset-v2 (extended to multi-turn) with synthetic dialogues produced by strong frontier models such as GPT-OSS-120B and Qwen3-235B variants.
This dataset is ready for commercial… See the full description on the dataset page: https://huggingface.co/datasets/Sidsidney/Nemotron-Instruction-Following-Chat-v1.Instructionfollowing
Basic Instruction Following Dataset
This dataset contains simple instruction–output pairs designed for training and evaluating instruction-following language models.
Dataset Structure
Each row contains:
instruction: a short natural language request
output: a concise and accurate response
Intended Use
Instruction-following model training
Supervised fine-tuning (SFT)
Educational and testing purposes
Example
{
"instruction": "What is machine… See the full description on the dataset page: https://huggingface.co/datasets/seto4/Instructionfollowing.SNU_Thunder-synthetic-instruction-following
Dataset Card for SNU Thunder Synthetic InstructionFollowing
Dataset Summary
This dataset was used as part of the post-training corpus for SnuLLM(to_fill).
This dataset consists of Korean and English question-answer pairs. Questions are sourced from publicly available datasets, and answers were generated using open large language models (Exaone 3.5, LLaMA 3.3, Qwen 2.5). It is intended for research and non-commercial use.
Supported Tasks
Tasks: Instruction… See the full description on the dataset page: https://huggingface.co/datasets/thunder-research-group/SNU_Thunder-synthetic-instruction-following.