datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
OpenCodeInstruct
OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs
Dataset Description
We introduce OpenCodeInstruct, the largest open-access instruction tuning dataset, comprising 5 million diverse samples. OpenCodeInstruct is designed for supervised fine-tuning (SFT).
Technical Report - Discover the methodology and technical details behind OpenCodeInstruct.
Github Repo - Access the complete pipeline used to perform SFT.
This dataset is ready for… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenCodeInstruct.OpenCodeReasoning
OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
Data Overview
OpenCodeReasoning is the largest reasoning-based synthetic dataset to date for coding, comprises 735,255 samples in Python across 28,319 unique competitive programming
questions. OpenCodeReasoning is designed for supervised fine-tuning (SFT).
Technical Report - Discover the methodology and technical details behind OpenCodeReasoning.
Github Repo - Access the complete pipeline used to… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenCodeReasoning.nvidia-OpenCodeInstruct-refined
nvidia-OpenCodeInstruct-refined
A strictly quality-filtered subset of nvidia/OpenCodeInstruct (5M examples). This is a strict subset of EER6/nvidia-OpenCodeInstruct-broad.
Filtering criteria
Both conditions must be satisfied:
Criterion
Threshold
LLM judge min score
= 5 (out of 5)
Unit test pass rate (average_test_score)
= 1.0
LLM judge min score is the minimum across all three dimensions in the llm_judgement field:
requirement_conformance — does the… See the full description on the dataset page: https://huggingface.co/datasets/EER6/nvidia-OpenCodeInstruct-refined.OpenCodeReasoning-2
OpenCodeReasoning-2: A Large-scale Dataset for Reasoning in Code Generation and Critique
Dataset Description
OpenCodeReasoning-2 is the largest reasoning-based synthetic dataset to date for coding, comprising 1.4M samples in Python and 1.1M samples in C++ across 34,799 unique competitive programming questions.
OpenCodeReasoning-2 is designed for supervised fine-tuning (SFT) tasks of code completion and code critique.
Github Repo - Access the complete pipeline used to… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenCodeReasoning-2.OpenCodeReasoning_messages
This is a transformation of the nvidia/OpenCodeReasoning dataset into a format that is more easily digestible by trainers.
OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
Data Overview
OpenCodeReasoning is the largest reasoning-based synthetic dataset to date for coding, comprises 735,255 samples in Python across 28,319 unique competitive programming
questions. OpenCodeReasoning is designed for supervised fine-tuning (SFT).
Technical Report -… See the full description on the dataset page: https://huggingface.co/datasets/sealad886/OpenCodeReasoning_messages.OpenCodeReasoning-2
OpenCodeReasoning-2: A Large-scale Dataset for Reasoning in Code Generation and Critique
Dataset Description
OpenCodeReasoning-2 is the largest reasoning-based synthetic dataset to date for coding, comprising 1.4M samples in Python and 1.1M samples in C++ across 34,799 unique competitive programming questions.
OpenCodeReasoning-2 is designed for supervised fine-tuning (SFT) tasks of code completion and code critique.
Github Repo - Access the complete pipeline used to… See the full description on the dataset page: https://huggingface.co/datasets/cublya/OpenCodeReasoning-2.OpenCodeInstruct
OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs
Dataset Description
We introduce OpenCodeInstruct, the largest open-access instruction tuning dataset, comprising 5 million diverse samples. OpenCodeInstruct is designed for supervised fine-tuning (SFT).
Technical Report - Discover the methodology and technical details behind OpenCodeInstruct.
Github Repo - Access the complete pipeline used to perform SFT.
This dataset is ready for… See the full description on the dataset page: https://huggingface.co/datasets/Jeremydh911/OpenCodeInstruct.NMT-opencode
OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
Data Overview
OpenCodeReasoning is the largest reasoning-based synthetic dataset to date for coding, comprises 735,255 samples in Python across 28,319 unique competitive programming
questions. OpenCodeReasoning is designed for supervised fine-tuning (SFT).
Technical Report - Discover the methodology and technical details behind OpenCodeReasoning.
Github Repo - Access the complete pipeline used to… See the full description on the dataset page: https://huggingface.co/datasets/Compumacy/NMT-opencode.opencode_reasoning_filtered
🧠 OpenCode Reasoning (Filtered)
Author: Parvesh Rawal — XenArcAILicense: Inherits from NVIDIA OpenCodeReasoningVersion: Filtered & Structured VariantTotal Examples: 567,850Total Size: 9GB (compressed)
🔍 Overview
This dataset is a curated and cleaned version of split_0 from nvidia/OpenCodeReasoning, optimized for code-level reasoning tasks and instruction tuning.
It’s designed to enhance logic understanding and multistep problem solving for LLMs.
📁 Features… See the full description on the dataset page: https://huggingface.co/datasets/Parveshiiii/opencode_reasoning_filtered.Qwen3-Coder-Next-OpenCode-Preference
Dataset Card — OpenCode Rejection Sampling (Preference)
Overview
This dataset contains 10,920 preference pairs for preference-based training (DPO, KTO, SimPO, ORPO, etc.) on competitive programming tasks. Each pair consists of:
Chosen: a candidate solution that passes 100% of test cases
Rejected: a candidate solution that fails, with a fine-grained rejection type label
Pairs are produced via rejection sampling with Qwen3-Coder-Next: 8 candidate solutions are… See the full description on the dataset page: https://huggingface.co/datasets/zake7749/Qwen3-Coder-Next-OpenCode-Preference.HyperThink-X-Nvidia-Opencode-Reasoning-200K
🔮 HyperThink
HyperThink is a premium, best-in-class dataset series capturing deep reasoning interactions between users and an advanced Reasoning AI system. Designed for training and evaluating next-gen language models on complex multi-step tasks, the dataset spans a wide range of prompts and guided thinking outputs.
🚀 Dataset Tiers
HyperThink is available in three expertly curated versions, allowing flexible scaling based on compute resources and training goals:… See the full description on the dataset page: https://huggingface.co/datasets/Sashvat/HyperThink-X-Nvidia-Opencode-Reasoning-200K.nvidia-OpenCodeInstruct-broad
nvidia-OpenCodeInstruct-broad
A quality-filtered subset of nvidia/OpenCodeInstruct (5M examples).
Filtering criteria
Both conditions must be satisfied:
Criterion
Threshold
LLM judge min score
>= 4 (out of 5)
Unit test pass rate (average_test_score)
>= 0.8
LLM judge min score is the minimum across all three dimensions in the llm_judgement field:
requirement_conformance — does the code do what the instruction asked?
logical_correctness — is the… See the full description on the dataset page: https://huggingface.co/datasets/EER6/nvidia-OpenCodeInstruct-broad.opencode-agentic-mini
OpenCode Agentic Dataset
A high-quality training dataset of real-world agentic coding sessions extracted from OpenCode CLI usage. Contains multi-turn conversations with tool calls, reasoning traces, and code editing across 45+ diverse software projects.
Dataset Summary
Split
Examples
Sessions
Train
18,549
~456
Validation
1,001
~51
Total
19,550
507
Estimated tokens: ~153M
Average tokens per example: ~7,800
Data Format
Each… See the full description on the dataset page: https://huggingface.co/datasets/Petrouil/opencode-agentic-mini.nvidia_opencodeinstruct-score-ge-0.9
dataset original: https://huggingface.co/datasets/nvidia/OpenCodeInstruct
filtered average_test_score >= 0.9
Dataset({
features: ['id', 'input', 'output', 'domain', 'generation_algorithm', 'llm_judgement', 'unit_tests', 'tests_execution_status', 'average_test_score'],
num_rows: 2194336
})
OpenCodeReasoning-Cleaned
OpenCodeReasoning-Cleaned
OpenCodeReasoning-Cleaned is a filtered and reformatted subset derived from nvidia/OpenCodeReasoning, prepared for streamlined supervised fine-tuning (SFT) of code-reasoning language models.
Dataset Summary
This dataset takes the first 100,000 rows of nvidia/OpenCodeReasoning, strips it down to only the two columns needed for instruction/response-style fine-tuning, renames them for clarity, cleans the data by removing null values and… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/OpenCodeReasoning-Cleaned.opencode_reasoning2_hard_codeforces2000_pr03_qwen35_fp8_thinking_annotated_10k_seed20260513
Qwen3.5 FP8 Annotations for 10K K2-Think OCR2 Coding Steps
This dataset contains Qwen3.5 FP8 step-level correctness annotations for K2-Think reasoning traces on a hard Codeforces subset of OpenCodeReasoning-2.
Summary
Source trace dataset: opencode_reasoning2_hard_codeforces2000_pr03_k2_thinking_extracted_pilot10
Source rows: 10 hard coding problem traces
Candidate step rule: claim with non-empty aligned_token_ids
Candidate steps: 15,267
Manifest-selected annotated… See the full description on the dataset page: https://huggingface.co/datasets/JingweiNi/opencode_reasoning2_hard_codeforces2000_pr03_qwen35_fp8_thinking_annotated_10k_seed20260513.