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.
bcb_dataDataScience-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.dataNemotron-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.cpt_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.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/GG-samrt/DataScience-Instruct-500K.INSTRUCT_JEV
INSTRUCT_JEV
INSTRUCT_JEV is an instruction corpus built from the TypeSafe AI documentation
for Jev, the first System One model. It is structured around the three TypeSafe
question primitives - Choice, Noul and Score - and mirrors the raw corpus
captured in deckerGUI-jev_corpus_RAW.
Credits
INSTRUCT_JEV is a DeckerGUI project and exists because of the work below.
Who
Contribution
Link
TypeSafe AI
Jev - the first System One model - and the Choice / Noul… See the full description on the dataset page: https://huggingface.co/datasets/ctaxnagomi/INSTRUCT_JEV.rag_instruct_benchmark_tester
Dataset Card for RAG-Instruct-Benchmark-Tester
Dataset Summary
This is an updated benchmarking test dataset for "retrieval augmented generation" (RAG) use cases in the enterprise, especially for financial services, and legal. This test dataset includes 200 questions with context passages pulled from common 'retrieval scenarios', e.g., financial news, earnings releases,
contracts, invoices, technical articles, general news and short texts.
The questions are segmented… See the full description on the dataset page: https://huggingface.co/datasets/llmware/rag_instruct_benchmark_tester.ds-coder-instruct-v2
Dataset Card for DS Coder Instruct v2 Dataset
Changes from v1:
Added WizardLM evol data science samples
Removed R samples from v2
DS Coder is a dataset for instruction fine tuning of language models. It is a specialized dataset focusing only on
data science (eg. plotting, data wrangling, machine learnig models, deep learning, and numerical computations). The dataset contains code examples both in Python (R samples were removed in v2).
The goal of this dataset is to enable… See the full description on the dataset page: https://huggingface.co/datasets/ed001/ds-coder-instruct-v2.qwen3_instruct_sft_2196031
OpenThinker3-qwen3-2196031
This model is a fine-tuned version of /home/rishabhtiwari/hf_cache/Qwen--Qwen3-30B-A3B-Base on the open_thoughts_3_small_instruct dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate:… See the full description on the dataset page: https://huggingface.co/datasets/rishabh2k1/qwen3_instruct_sft_2196031.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.gpt-oss-120b-Reasoning-InstructionLlama-Breeze2-8B-Instruct-eval-logs-and-scoresllama-3.2-3B-f1-instruct-eval-logs-and-scoresLlama-3.3-70B-Instruct-eval-logs-and-scoresLlama-3.2-3B-Instruct-eval-logs-and-scoresLlama-3.1-Taiwan-8B-Instruct-eval-logs-and-scoresLlama-3.1-8B-Instruct-eval-logs-and-scoresLlama-3-Taiwan-70B-Instruct-eval-logs-and-scoresqwen3_instruct_sft_2196030
OpenThinker3-qwen3-2196030
This model is a fine-tuned version of /home/rishabhtiwari/hf_cache/Qwen--Qwen3-30B-A3B-Base on the open_thoughts_3_small_instruct dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate:… See the full description on the dataset page: https://huggingface.co/datasets/rishabh2k1/qwen3_instruct_sft_2196030.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/Fan0718/DataScience-Instruct-500K.AquilaEdu-Instruct
Data Description
The dataset comes from 5 different tasks: math word problems, commonsense reasoning, reading comprehension, subject knowledge, and instruction following. It involves 11 datasets, including Math23k, Ape210k, MetaMath, OpenbookQA, CommensenseQA, Arc-e, Arc-c, Race, MCTest. COIG, and Taoli.
Data Selection
We first supplemented the COT process for the data and translated some English data sets into Chinese data sets.
Deita: We score the complexity of… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/AquilaEdu-Instruct.hex-lora-opus-magnum-instructions-only-results
hex-lora-opus-magnum-instructions-only-results
Held-out evaluation logs for the same 6-LoRA RL sweep as
opus-magnum-rl-eval, but on a much harder eval task:
the 57-puzzle "instructions-only" set drawn from the
Opus Magnum campaign + curated
holdout puzzles. The agent runs an interactive Python REPL and must submit()
a working .solution file to the in-game verifier.
`57 puzzles × 6 epochs × (9 LoRA-sweep variants + 2 27B mt=4096 reruns
2 Gemini Flash baselines) = 4446 trajectories`.… See the full description on the dataset page: https://huggingface.co/datasets/robhaisfield/hex-lora-opus-magnum-instructions-only-results.Qwen__Qwen2.5-7B-Instruct-details
Dataset Card for Evaluation run of Qwen/Qwen2.5-7B-Instruct
Dataset automatically created during the evaluation run of model Qwen/Qwen2.5-7B-Instruct
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 2 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 pointing to the latest results.
An… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/Qwen__Qwen2.5-7B-Instruct-details.threejs-gamecode-instruct-v3-ultra
Three.js GameCode Instruct v3 Ultra
This is a large synthetic/original instruction dataset for training or testing LLM behavior around Three.js, browser game development, gameplay programming, debugging, optimization, architecture, and general coding.
Important note
This dataset is synthetic and programmatically generated from original templates. It is designed as a useful starting point for experiments, not as a fully hand-curated gold-standard benchmark.
No… See the full description on the dataset page: https://huggingface.co/datasets/agagasf123123/threejs-gamecode-instruct-v3-ultra.Qwen__Qwen2.5-72B-Instruct-details
Dataset Card for Evaluation run of Qwen/Qwen2.5-72B-Instruct
Dataset automatically created during the evaluation run of model Qwen/Qwen2.5-72B-Instruct
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 2 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 pointing to the latest results.
An… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/Qwen__Qwen2.5-72B-Instruct-details.xl-instruct
Dataset Card for XL-Instruct
This dataset card provides a summary of the XL-Instruct dataset, a resource for advancing the cross-lingual capabilities of Large Language Models. It was introduced in the paper XL-Instruct: Synthetic Data for Cross-Lingual Open-Ended Generation.
Dataset Details
Dataset Description
XL-Instruct is a high-quality, large-scale synthetic dataset designed to fine-tune LLMs for cross-lingual open-ended generation. The core task involves… See the full description on the dataset page: https://huggingface.co/datasets/viyer98/xl-instruct.synthid-qwen3-4b-instruct-2507-wildchat
Qwen3-4B SynthID three-arm corpus
This export contains aligned unwatermarked, SynthID key-A, and SynthID key-B
responses from Qwen/Qwen3-4B-Instruct-2507. Matched splits share prompts
and request seeds across configurations; unmatched splits use mutually disjoint
prompt pools.
Export complete for its source work queue: true.
Generation profile
Model revision: cdbee75f17c01a7cc42f958dc650907174af0554
Native model dtype: bfloat16
Maximum generated tokens: 4096… See the full description on the dataset page: https://huggingface.co/datasets/xlr8harder/synthid-qwen3-4b-instruct-2507-wildchat.Qwen__Qwen2.5-32B-Instruct-details
Dataset Card for Evaluation run of Qwen/Qwen2.5-32B-Instruct
Dataset automatically created during the evaluation run of model Qwen/Qwen2.5-32B-Instruct
The dataset is composed of 38 configuration(s), each one corresponding 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 pointing to the latest results.
An… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/Qwen__Qwen2.5-32B-Instruct-details.