datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
KodCode-V1-SFT-R1
🐱 KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding
KodCode is the largest fully-synthetic open-source dataset providing verifiable solutions and tests for coding tasks. It contains 12 distinct subsets spanning various domains (from algorithmic to package-specific knowledge) and difficulty levels (from basic coding exercises to interview and competitive programming challenges). KodCode is designed for both supervised fine-tuning (SFT) and RL tuning.
🕸️… See the full description on the dataset page: https://huggingface.co/datasets/KodCode/KodCode-V1-SFT-R1.pa-warm-start-sft-heavy-25b-mix
geodesic-research/pa-warm-start-sft-heavy-25b-mix
Auto-generated by dataset-builder.
Each config below is a separate dataset produced from a versioned YAML build
config. Load with:
from datasets import load_dataset
ds = load_dataset("geodesic-research/pa-warm-start-sft-heavy-25b-mix", "<config_name>", revision="<commit-sha>")
Pin revision= to the specific commit SHA you want; without it, you get the
current HEAD of the dataset repo, which may change when the builder re-pushes.… See the full description on the dataset page: https://huggingface.co/datasets/geodesic-research/pa-warm-start-sft-heavy-25b-mix.Eurus-2-7B-SFT_eval_2e29
mlfoundations-dev/Eurus-2-7B-SFT_eval_2e29
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
AIME25
HLE
LiveCodeBenchv5
Accuracy
2.3
21.0
30.6
11.0
11.4
10.4
6.8
1.5
2.1
1.3
4.1
4.4
AIME24
Average Accuracy: 2.33% ± 0.67%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
0.00%
0
30
2
3.33%
1… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/Eurus-2-7B-SFT_eval_2e29.Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection
🧬 Omni-Frontier Collection
Cybersecurity · Coding · Math · Science · RSI Reasoning — one unified SFT package
A unified, deduplicated, fully-browsable distillation & SFT corpus — every row real, every row visible.
📖 Jump to
What's inside · 🔁 Aggregation audit · 🛡 Cybersecurity · 💻 Coding · 🏭 Distillation deep-dive · 🔁 RSI · 🧮 Math/Science/More · 🎓 Training guide · 🔎 Browsing · 🧹 Quality · 🗺 Roadmap · 📄 License… See the full description on the dataset page: https://huggingface.co/datasets/SHSLab/Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection.pa-warm-start-sft-xl-50b-mix
geodesic-research/pa-warm-start-sft-xl-50b-mix
Auto-generated by dataset-builder.
Each config below is a separate dataset produced from a versioned YAML build
config. Load with:
from datasets import load_dataset
ds = load_dataset("geodesic-research/pa-warm-start-sft-xl-50b-mix", "<config_name>", revision="<commit-sha>")
Pin revision= to the specific commit SHA you want; without it, you get the
current HEAD of the dataset repo, which may change when the builder re-pushes.… See the full description on the dataset page: https://huggingface.co/datasets/geodesic-research/pa-warm-start-sft-xl-50b-mix.sorrel-sft-voiceKodCode-V1-SFT-4o
🐱 KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding
KodCode is the largest fully-synthetic open-source dataset providing verifiable solutions and tests for coding tasks. It contains 12 distinct subsets spanning various domains (from algorithmic to package-specific knowledge) and difficulty levels (from basic coding exercises to interview and competitive programming challenges). KodCode is designed for both supervised fine-tuning (SFT) and RL tuning.
🕸️… See the full description on the dataset page: https://huggingface.co/datasets/KodCode/KodCode-V1-SFT-4o.pa-warm-start-sft-xl-smokemoss-002-sft-data
Dataset Card for "moss-002-sft-data"
Dataset Summary
An open-source conversational dataset that was used to train MOSS-002. The user prompts are extended based on a small set of human-written seed prompts in a way similar to Self-Instruct. The AI responses are generated using text-davinci-003. The user prompts of en_harmlessness are from Anthropic red teaming data.
Data Splits
name
# samples
en_helpfulness.json
419049
en_honesty.json
112580… See the full description on the dataset page: https://huggingface.co/datasets/OpenMOSS-Team/moss-002-sft-data.ReasonXL-SFT
ReasonXL: A Multilingual Cross-Domain Reasoning Corpus
ReasonXL is a large-scale multilingual reasoning corpus spanning five languages, with 2,538,450 positionally aligned examples per language (12,692,250 rows total). It is designed to support supervised fine-tuning of reasoning models with in-language chain-of-thought traces across diverse technical domains.
Data Generation
English source samples were drawn from 10 existing reasoning datasets, filtered and… See the full description on the dataset page: https://huggingface.co/datasets/toroe/ReasonXL-SFT.fable-5-sft-traces
Fable-5 SFT Traces
Author / maintainer: kelexine (github.com/kelexine)
A cleaned, anonymised, schema-normalised derivative of
Kelexine/Fable-5-traces
— agentic traces from Fable-5 (claude-fable-5), the model now publicly
known as Claude Mythos — Anthropic's top-of-family frontier model at time
of collection.
The dataset supports three fine-tuning shapes off a single JSONL with no
preprocessing required:
Mode
Fields used
Full SFT (thinking + response)
messages or… See the full description on the dataset page: https://huggingface.co/datasets/kelexine/fable-5-sft-traces.smolmo-sft-v2-seqlen64k
smolmo-sft-v2-seqlen64k
A supervised fine-tuning (SFT) dataset of math problems with full chain-of-thought solutions,
formatted for the Olmo 3 "Thinking" models.
2,813,055 examples · ~37.9 B tokens.
Three task families: proofs, numeric-answer problems, and tool-augmented (Python) problems.
Every assistant turn carries an explicit <think> … </think> reasoning trace before the answer.
Olmo 3 native chat + function-calling format; every example fits within a 64k-token context.… See the full description on the dataset page: https://huggingface.co/datasets/chankhavu/smolmo-sft-v2-seqlen64k.VR-X-SFT-RL
VR-X: Visual Reasoning Benchmark for UniVR
VR-X contains three independent data blocks:
SFT data organized by capability.
VR-X-RL data for visual-reasoning reinforcement learning.
VR-X-Eval held-out evaluation data.
VR-X-RL and VR-X-Eval are independent from SFT and must be loaded separately.
Public repository paths use anonymous source codes; no source-to-code mapping is
published.
Repository layout
.
├── Robot Manipulation/ # SFT only
│ └── RM-###/
│… See the full description on the dataset page: https://huggingface.co/datasets/ByteDance/VR-X-SFT-RL.pa-warm-start-sft-medium-5b-mix
geodesic-research/pa-warm-start-sft-medium-5b-mix
Auto-generated by dataset-builder.
Each config below is a separate dataset produced from a versioned YAML build
config. Load with:
from datasets import load_dataset
ds = load_dataset("geodesic-research/pa-warm-start-sft-medium-5b-mix", "<config_name>", revision="<commit-sha>")
Pin revision= to the specific commit SHA you want; without it, you get the
current HEAD of the dataset repo, which may change when the builder re-pushes.… See the full description on the dataset page: https://huggingface.co/datasets/geodesic-research/pa-warm-start-sft-medium-5b-mix.0399-tv-valid-clean-sft-tokenized-llmjp4-8btts-realspeech-sft-en-de
LAION TTS Real-Speech SFT — English + German, emotion-balanced
1,947,272 real recorded utterances — no synthetic voices — selected from freely-licensed corpora
and balanced across 40 emotions x 2 languages. 6,996 hours,
313,844,544 MOSS frames (3,766,134,528 audio tokens), 79,337,527 aligned words.
Each row is a self-contained TTS example: a corrected procedural caption, the transcript with
word-level timestamps, the original audio, and the target MOSS-Audio-Tokenizer-v2 codes.… See the full description on the dataset page: https://huggingface.co/datasets/laion/tts-realspeech-sft-en-de.smolkalam-arabic-conversational-sft
SmolKalam
SmolKalam is a quality-filtered Arabic SFT dataset of 1,790,478 examples (~2.45B tokens), built as an ensemble translation of SmolTalk2. It covers multi-turn dialogue (23% of rows), reasoning traces (19% carry <think>), tool and function calling (4.4%), and long context, categories that are underrepresented in existing Arabic post-training data. The SmolTalk2 source mixtures are kept as subsets.
Released with the paper SmolKalam: Ensemble Quality-Filtered Translation… See the full description on the dataset page: https://huggingface.co/datasets/AdaMLLab/smolkalam-arabic-conversational-sft.gigaverbo-v2-sft
GigaVerbo-v2 SFT: A Large-Scale Portuguese Instruction-Tuning Dataset
Dataset Summary
GigaVerbo-v2 SFT is a large-scale instruction-tuning dataset designed for supervised fine-tuning of language models in Portuguese. The dataset comprises approximately 2.1 billion tokens (~4.4 GB) across 4 million instruction-following examples, organized into 12 distinct task categories. It is entirely composed of high-quality, LLM-generated data that has been carefully curated and… See the full description on the dataset page: https://huggingface.co/datasets/Polygl0t/gigaverbo-v2-sft.maniskill3-sft-rgb-lerobot-1200epThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "maniskill3_sim",
"total_episodes": 1200,
"total_frames": 171480,
"total_tasks": 6,
"total_videos": 0,
"total_chunks": 2,
"chunks_size": 1000,
"fps": 20,
"splits": {
"train": "0:1200"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": null,
"features":… See the full description on the dataset page: https://huggingface.co/datasets/onnoboru/maniskill3-sft-rgb-lerobot-1200ep.rubrichub-sft-judgment-genlm-eval-results-princeton-nlp-Llama-3-Base-8B-SFT-RDPO-private
Dataset Card for Evaluation run of princeton-nlp/Llama-3-Base-8B-SFT-RDPO
Dataset automatically created during the evaluation run of model princeton-nlp/Llama-3-Base-8B-SFT-RDPO
The dataset is composed of 62 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 7 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… See the full description on the dataset page: https://huggingface.co/datasets/nyu-dice-lab/lm-eval-results-princeton-nlp-Llama-3-Base-8B-SFT-RDPO-private.pa-warm-start-sft-xl-calibrationDolci-Think-SFT-translated
Dolci-Think-SFT-translated
Machine translations of the Dolci-Think-SFT-32B dataset, produced with gemma-4-31B-it. The samples selected for translation are those where content_quality == "excellent" according to the propella annotations.
Columns
Each row is a translated conversation plus the result of a post-translation quality filter:
id — source record id.
messages — the translated conversation (list of {content, role}).
filter_pass — true if the row passed… See the full description on the dataset page: https://huggingface.co/datasets/openeurollm/Dolci-Think-SFT-translated.LLM-fingerprinted-SFTbhasha-sft
Bhasha SFT
Bhasha SFT is a massive collection of multiple open sourced Supervised Fine-Tuning datasets for training Multilingual
Large Language Models. The dataset contains collation of over 13 million instances of
instruction-response data for 3 Indian languages (Hindi, Gujarati, Bengali) and English having both human annotated and synthetic data.
Curated by: Soket AI Labs
Language(s) (NLP): [English, Hindi, Bengali, Gujarati]
License: [cc-by-4.0, apache-2.0, mit]… See the full description on the dataset page: https://huggingface.co/datasets/soketlabs/bhasha-sft.Soofi-Think-SFT-10B-multilingual
ReasonXL: A Multilingual Cross-Domain Reasoning Corpus
ReasonXL is a large-scale multilingual reasoning corpus spanning 5 languages and ~44B tokens in total. It is designed to support supervised fine-tuning of reasoning models with in-language chain-of-thought traces across diverse technical domains.
Data Generation
English source samples were drawn from 10 existing reasoning datasets, filtered and quality-annotated using ellamind/propella-1-4b, and then translated into… See the full description on the dataset page: https://huggingface.co/datasets/toroe/Soofi-Think-SFT-10B-multilingual.Omni-Frontier-Distillation-SFT-Cyber-security-Coding-dataset-collection-v2
🧬 Omni-Frontier Collection
Cybersecurity · Coding · Math · Science · RSI Reasoning — one unified SFT package
A unified, deduplicated, fully-browsable distillation & SFT corpus — every row real, every row visible.
📖 Jump to
What's inside · 🔁 Aggregation audit · 🛡 Cybersecurity · 💻 Coding · 🏭 Distillation deep-dive · 🔁 RSI · 🧮 Math/Science/More · 🎓 Training guide · 🔎 Browsing · 🧹 Quality · 🗺 Roadmap · 📄 License… See the full description on the dataset page: https://huggingface.co/datasets/Manusagents/Omni-Frontier-Distillation-SFT-Cyber-security-Coding-dataset-collection-v2.pa-warm-start-sft-heavy-25b-mix-longpa-warm-start-sft-xl-1b-smokeround2-oss-matched
round2-oss-matched — 第二轮 4 组实验数据（每组 10 节点，共 40）
代码：repo 分支 claude/round2-matched-compute（先 git fetch origin && git merge origin/claude/round2-matched-compute）。
目录：
exp0_20b/node00..04/pool.jsonl # 实验 0:shard-05 修复重跑(20B)
exp0_120b/node00..04/pool.jsonl # 实验 0:同上(120B)
exp1_20b/node00..09/{seeds,budgets,pool}.jsonl # 实验 1:20B token 对齐独立采样
exp2_120b/node00..09/{seeds,budgets,pool}.jsonl # 实验 2:120B 同上
exp3_120b/node00..09/{ck_nonsat/,nonsat_seeds,budgets,pool… See the full description on the dataset page: https://huggingface.co/datasets/tts-sft/round2-oss-matched.