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.opencodeinstruct-curatednvidia-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.opc-sft-stage2
OpenCoder Dataset
The OpenCoder dataset is composed of the following datasets:
opc-sft-stage1: the sft data used for opencoder sft-stage1
opc-sft-stage2: the sft data used for opencoder sft-stage2 <-- you are here
opc-annealing-corpus: the synthetic data & algorithmic corpus used for opencoder annealing
opc-fineweb-code-corpus: the code-related page recalled from fineweb
opc-fineweb-math-corpus: the math-related page recalled from finewebrefineCode-code-corpus-meta: the meta-data… See the full description on the dataset page: https://huggingface.co/datasets/OpenCoder-LLM/opc-sft-stage2.opc-annealing-corpus
OpenCoder Dataset
The OpenCoder dataset is composed of the following datasets:
opc-sft-stage1: the sft data used for opencoder sft-stage1
opc-sft-stage2: the sft data used for opencoder sft-stage2
opc-annealing-corpus: the synthetic data & algorithmic corpus used for opencoder annealing <-- you are here
fineweb-code-corpus: the code-related page recalled from fineweb
fineweb-math-corpus: the math-related page recalled from finewebrefineCode-code-corpus-meta: the meta-data of… See the full description on the dataset page: https://huggingface.co/datasets/OpenCoder-LLM/opc-annealing-corpus.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.opc-fineweb-code-corpus
OpenCoder Dataset
The OpenCoder dataset is composed of the following datasets:
opc-sft-stage1: the sft data used for opencoder sft-stage1
opc-sft-stage2: the sft data used for opencoder sft-stage2
opc-annealing-corpus: the synthetic data & algorithmic corpus used for opencoder annealing
opc-fineweb-code-corpus: the code-related page recalled from fineweb <-- you are here
opc-fineweb-math-corpus: the math-related page recalled from finewebrefineCode-code-corpus-meta: the meta-data… See the full description on the dataset page: https://huggingface.co/datasets/OpenCoder-LLM/opc-fineweb-code-corpus.opencode_seed2.1_expert_skill_round_00opencode-cached-binariesopc-sft-stage1
OpenCoder Dataset
The OpenCoder dataset is composed of the following datasets:
opc-sft-stage1: the sft data used for opencoder sft-stage1 <-- you are here
opc-sft-stage2: the sft data used for opencoder sft-stage2
opc-annealing-corpus: the synthetic data & algorithmic corpus used for opencoder annealing
opc-fineweb-code-corpus: the code-related page recalled from fineweb
opc-fineweb-math-corpus: the math-related page recalled from finewebrefineCode-code-corpus-meta: the meta-data… See the full description on the dataset page: https://huggingface.co/datasets/OpenCoder-LLM/opc-sft-stage1.opencode_seed2.1_expert_skill_round_00_20260712opencode-ecosystem-core
OpenCode Ecosystem Core
Ecossistema Python completo para orquestração de tarefas, memória metacognitiva, especificações SDD/TDD, integrações MCP e fluxos de pesquisa científica.
Visão Geral
O OpenCode Ecosystem Core é um ecossistema Python de código aberto que organiza o ciclo perceber → especificar → delegar → executar → verificar → refletir. Ele combina:
Interface de linha de comando (CLI) para diagnóstico, pesquisa e apresentações
Registro de… See the full description on the dataset page: https://huggingface.co/datasets/marceloclaro/opencode-ecosystem-core.Nemotron-SFT-OpenCode-v1
Dataset Description:
Nemotron-SFT-OpenCode-v1 is an agentic instruction tuning dataset that enhances the ability of Large Language Models (LLMs) to operate within the OpenCode Command Line Interface (CLI) framework and instills simple capabilities such as tool calling and agent skills.
This dataset is ready for commercial/non-commercial use.
Dataset Subsets:
Nemotron-SFT-OpenCode-v1 contains the following subsets, where the questions and agent skills are synthetically… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-OpenCode-v1.opencode_seed2.1_video_badcase_debug_round_00RefineCode-code-corpus-metaThis dataset consists of meta information (including the repository name and file path) of the raw code data from RefineCode. You can collect those files referring to this metadata and reproduce RefineCode!
Note: Currently, we have uploaded the meta data covered by The Stack V2 (About 50% file volume). Due to complex legal considerations, we are unable to provide the complete source code currently. We are working hard to make the remaining part available.
RefineCode is a high-quality… See the full description on the dataset page: https://huggingface.co/datasets/OpenCoder-LLM/RefineCode-code-corpus-meta.OpenCodeReasoning2OpenCodeReasoning_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.opencode_seed2.1_expert_without_reproduce_round_00opencoderinstruct_trajectoryOpenCodeReasoning-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.Opencode1OpenCodeInstruct-Clean
OpenCodeInstruct Clean
High-quality Python code generation dataset with duplication markers and complexity metrics.
Derived from nvidia/OpenCodeInstruct
after applying strict quality gates.
Quick Stats
Metric
Value
Total rows
388,629
Columns
54
Python-parsable
100.0%
Overview
This dataset contains 388,629 high-quality Python code generation examples
extracted from the nvidia/OpenCodeInstruct corpus.
Each row has been… See the full description on the dataset page: https://huggingface.co/datasets/JetBrains-Research/OpenCodeInstruct-Clean.OpenCodeGeneticInstruct
OpenCodeGeneticInstruct: A large-scale dataset of coding instructions for improving the code generation capabilities of LLMs
Data Overview
OpenCodeGeneticInstruct comprises more than 15M coding instructions in python which is generated synthetically with the Genetic-Instruct [1] approach.
This dataset can be used for supervised fine-tuning (SFT) of LLMs to improve their code genearation capability.
Each sample includes a coding question/instruction and its corrsponding… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenCodeGeneticInstruct.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.opc-fineweb-math-corpus
OpenCoder Dataset
The OpenCoder dataset is composed of the following datasets:
opc-sft-stage1: the sft data used for opencoder sft-stage1
opc-sft-stage2: the sft data used for opencoder sft-stage2
opc-annealing-corpus: the synthetic data & algorithmic corpus used for opencoder annealing
opc-fineweb-code-corpus: the code-related page recalled from fineweb
opc-fineweb-math-corpus: the math-related page recalled from fineweb <-- you are here
refineCode-code-corpus-meta: the… See the full description on the dataset page: https://huggingface.co/datasets/OpenCoder-LLM/opc-fineweb-math-corpus.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.OpenCodeReasoning-formattedllm-verifier-freelancer-qwen3.5-122b-131k-opencode-traces
Agent trace dataset
Decoding the literal token IDs
The prompt_token_ids / completion_token_ids / logprobs columns are the
verbatim tokens the serving engine emitted, stored PER AGENT STEP as a
list-of-lists (one inner list per turn). To turn them back into text you MUST
use the exact tokenizer the model was served with — a generic same-family
tokenizer will decode word tokens to garbage.
Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/llm-verifier-freelancer-qwen3.5-122b-131k-opencode-traces.nemotron-gym-instruction-following-structured-qwen3.5-122b-131k-opencode-traces
Agent trace dataset
Decoding the literal token IDs
The prompt_token_ids / completion_token_ids / logprobs columns are the
verbatim tokens the serving engine emitted, stored PER AGENT STEP as a
list-of-lists (one inner list per turn). To turn them back into text you MUST
use the exact tokenizer the model was served with — a generic same-family
tokenizer will decode word tokens to garbage.
Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/nemotron-gym-instruction-following-structured-qwen3.5-122b-131k-opencode-traces.