datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Magicoder-OSS-Instruct-75KThis is the OSS-Instruct dataset generated by gpt-3.5-turbo-1106 developed by OpenAI. Please pay attention to OpenAI's usage policy when adopting this dataset: https://openai.com/policies/usage-policies.
Infinity-Instruct
Infinity Instruct
Beijing Academy of Artificial Intelligence (BAAI)
[Paper][Code][🤗]
The quality and scale of instruction data are crucial for model performance. Recently, open-source models have increasingly relied on fine-tuning datasets comprising millions of instances, necessitating both high quality and large scale. However, the open-source community has long been constrained by the high costs associated with building such extensive and high-quality instruction… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/Infinity-Instruct.bcb_datafull-math-private-n256-Qwen2.5-3B-Instruct-boncode_contests_instruct
Dataset Card for "code_contests_instruct"
The deepmind/code_contests dataset formatted as markdown-instruct for text generation training.
There are several different configs. Look at them. Comments:
flesch_reading_ease is computed on the description col via textstat
hq means that python2 (aka PYTHON in language column) is dropped, and keeps only rows with flesch_reading_ease 75 or greater
min-cols drops all cols except language and text
possible values for language are {'CPP'… See the full description on the dataset page: https://huggingface.co/datasets/BEE-spoke-data/code_contests_instruct.full-math-private-n256-Llama-3.2-3B-Instruct-bondetails_grimjim__Llama-3-Instruct-8B-SimPO-SPPO-Iter3-merge
Dataset Card for Evaluation run of grimjim/Llama-3-Instruct-8B-SimPO-SPPO-Iter3-merge
Dataset automatically created during the evaluation run of model grimjim/Llama-3-Instruct-8B-SimPO-SPPO-Iter3-merge.
The dataset is composed of 136 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is… See the full description on the dataset page: https://huggingface.co/datasets/OALL/details_grimjim__Llama-3-Instruct-8B-SimPO-SPPO-Iter3-merge.DataScience-Instruct-500K
DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
Authors: Shaolei Zhang, Ju Fan*, Meihao Fan, Guoliang Li, Xiaoyong Du
DeepAnalyze is the first agentic LLM for autonomous data science. It can autonomously complete a wide range of data-centric tasks without human intervention, supporting:
🛠 Entire data science pipeline: Automatically perform any data science tasks such as data preparation, analysis, modeling, visualization, and report generation.
🔍… See the full description on the dataset page: https://huggingface.co/datasets/RUC-DataLab/DataScience-Instruct-500K.preprocessed-full-math-private-n256-Llama-3.2-3B-Instruct-bonpython-text-copilot-training-instruct-ai-research-2024-02-03
Python Copilot Instructions on How to Code using Alpaca and Yaml
Training and test datasets for building coding multimodal models that understand how to use the open source GitHub projects for the Agora Open Source AI Research Lab:
Agora GitHub Organization
Agora Hugging Face
This dataset is the 2024-02-03 update for the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.
Details… See the full description on the dataset page: https://huggingface.co/datasets/matlok/python-text-copilot-training-instruct-ai-research-2024-02-03.Sujet-Finance-Instruct-177k
Sujet Finance Dataset Overview
The Sujet Finance dataset is a comprehensive collection designed for the fine-tuning of Language Learning Models (LLMs) for specialized tasks in the financial sector. It amalgamates data from 18 distinct datasets hosted on HuggingFace, resulting in a rich repository of 177,597 entries. These entries span across seven key financial LLM tasks, making Sujet Finance a versatile tool for developing and enhancing financial applications of AI.… See the full description on the dataset page: https://huggingface.co/datasets/sujet-ai/Sujet-Finance-Instruct-177k.cybersecurity_32k_instruction_input_output
Dataset Card
The dataset Q&As are focused on identification of cyber threats, and text classification under the NIST taxonomy and ITC EBA IT risk classes
Dataset Details
Dataset Description
This dataset includes a mix of public reports and news and aims to be used for cyber security risk model training.
It includes 32k examples with instruction, input and output. The latter is the output from GPT.
Curated by: [Vanessa Lopes]
Language [EN]
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Vanessasml/cybersecurity_32k_instruction_input_output.datafull-math-private-Qwen3-4B-Instruct-2507-bonLlama-3.1-8B-Instruct_eval_5554
mlfoundations-dev/Llama-3.1-8B-Instruct_eval_5554
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
HLE
HMMT
AIME25
LiveCodeBenchv5
Accuracy
4.7
15.8
43.2
44.7
14.1
25.8
13.1
2.1
6.7
17.0
0.3
0.3
8.9
AIME24
Average Accuracy: 4.67% ± 0.84%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
3.33%… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/Llama-3.1-8B-Instruct_eval_5554.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.full-math-private-Qwen2.5-3B-Instruct-bondetails_princeton-nlp__Llama-3-8B-ProLong-512k-Instruct
Dataset Card for Evaluation run of princeton-nlp/Llama-3-8B-ProLong-512k-Instruct
Dataset automatically created during the evaluation run of model princeton-nlp/Llama-3-8B-ProLong-512k-Instruct.
The dataset is composed of 136 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always… See the full description on the dataset page: https://huggingface.co/datasets/OALL/details_princeton-nlp__Llama-3-8B-ProLong-512k-Instruct.4B-Instruct-reason-only.stride-1.k-8.statml-arxiv.qwen3-ids
4B-Instruct-reason-only.stride-1.k-8.statml-arxiv.qwen3-ids
Thoughts for next-token prediction on k=8 token chunks of JackHsieh/statML-arxiv-40M-20M, generated by
Qwen3-4B-Instruct-2507. Each thought is a few dense sentences of reasoning about the next
8 tokens after a cut, written from the document prefix alone — the generator never sees the
continuation. Stored thought_text includes the <thought>/</thought> wrapper.
This is the small-generator parity counterpart of… See the full description on the dataset page: https://huggingface.co/datasets/JackHsieh/4B-Instruct-reason-only.stride-1.k-8.statml-arxiv.qwen3-ids.full-math-private-n256-flip719-Qwen3-4B-Instruct-2507-boncpt_instruction_datasets
Instruction datasets
Collection of synthetic instruction datasets used during the continued pretraining of Model-small-instr-1, Model-small-instr-2 and Model-small-instr-3. You can currently find these models under: Llama-3.1-Carballo-Instr1 and Llama-3.1-Carballo-Instr3.
Dataset creation
Datasets were created using two different techniques:
Adapting already existing datasets or corpora by modifying their format to make them suitable for including instructions during… See the full description on the dataset page: https://huggingface.co/datasets/proxectonos/cpt_instruction_datasets.dolma_20bn_no_instructpython-text-copilot-training-instruct
Python Copilot Instructions on How to Code using Alpaca and Yaml
This dataset is a subset of the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.
Details
Each row contains python code, either a class method or a global function, imported modules, base classes (if any), exceptions (ordered based off the code), returns (ordered based off the code), arguments (ordered based off the… See the full description on the dataset page: https://huggingface.co/datasets/matlok/python-text-copilot-training-instruct.Dolci-Instruct-RL
Dolci-Instruct-RL
Dataset Summary
Dolci-Instruct-RL is the reinforcement learning dataset used to train the Olmo-3-7B-Instruct model.It contains 169,964 prompts spanning:
Math
Code
Precise Instruction Following
General Chat
The dataset aggregates multiple curated sources, applies extensive filtering, and produces a unified RL-ready prompt set.
Dataset Composition
Total Samples: 169,964
Original Dataset Contribution… See the full description on the dataset page: https://huggingface.co/datasets/allenai/Dolci-Instruct-RL.t5gemma2-indonesia-instruct-v1
T5Gemma-2 Indonesian Instruct — Mono-Repo
Satu repositori dataset HF untuk seluruh data pelatihan T5-Gemma-2 bahasa Indonesia.
Diorganisasi per fungsi (fondasi → spesifik → preferensi) dengan folder/subfolder,
setiap config = folder dan berisi split train + validation (80:20) di level percakapan.
Struktur (by fungsi)
t5gemma2-indonesia-instruct-v1/
├── README.md
├── manifest.json
├── chat_idx_map.json
├── foundation/ ← FASE 1 · fondasi Bahasa… See the full description on the dataset page: https://huggingface.co/datasets/daruokta/t5gemma2-indonesia-instruct-v1.python-text-copilot-training-instruct-ai-research-2024-02-11
Python Copilot Instructions on How to Code using Alpaca and Yaml
Training and test datasets for building coding multimodal models that understand how to use the open source GitHub projects for the Autogen and multimodal Qwen AI project:
Qwen
Qwen Agent
Qwen VL Chat
Qwen Audio
This dataset is the 2024-02-11 update for the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.
Details… See the full description on the dataset page: https://huggingface.co/datasets/matlok/python-text-copilot-training-instruct-ai-research-2024-02-11.full-aime_2024-n256-ctx8k-Qwen3-4B-Instruct-2507-bonfull-aime_2023-n256-ctx8k-Qwen3-4B-Instruct-2507-bonDataScience-Instruct-500K
DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
Authors: Shaolei Zhang, Ju Fan*, Meihao Fan, Guoliang Li, Xiaoyong Du
DeepAnalyze is the first agentic LLM for autonomous data science. It can autonomously complete a wide range of data-centric tasks without human intervention, supporting:
🛠 Entire data science pipeline: Automatically perform any data science tasks such as data preparation, analysis, modeling, visualization, and report generation.
🔍… See the full description on the dataset page: https://huggingface.co/datasets/GG-samrt/DataScience-Instruct-500K.smollm2-135m-instruct-SAE
Layer
EV
Mean L0
Recon Loss
Dead %
0
0.9480
48.74
0.2074
0.0
1
0.9599
43.65
0.3298
0.0
2
0.9631
46.81
0.5021
0.0
3
0.9508
46.56
0.7462
0.0
4
0.9463
46.23
0.8936
0.0
5
0.9350
47.57
1.1605
0.0
6
0.9306
48.44
1.3838
0.0
7
0.9318
49.51
1.5446
0.0
8
0.9432
46.52
1.6598
0.0
9
0.9373
47.15
2.0706
0.0
10
0.9348
45.53
2.2983
0.0
11
0.9905
48.58
5.8113
0.0
12
0.9901
48.42
6.1039
0.0
13
0.9891
46.15
6.9692
0.0
14
0.9884
44.76
7.1844
0.0
15
0.9863
47.63
8.6521
0.0… See the full description on the dataset page: https://huggingface.co/datasets/juiceb0xc0de/smollm2-135m-instruct-SAE.