datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.kimi-k3-coding-and-debugging-traces
Kimi K3 Coding, Tool Use & Instruction Following Traces
582 TRAJECTORIES · 3,956 TRAINING ROWS · 3 MB PARQUET · 72 MB JSONL
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Behavior-preserving instruction-following, tool-use, and agent trajectories
from Kimi K3 (moonshotai/kimi-k3). The category and row-share tables
below describe the actual mix seen during training rather than assuming a… See the full description on the dataset page: https://huggingface.co/datasets/greghavens/kimi-k3-coding-and-debugging-traces.fable-5-coding-and-debugging-traces
Claude Fable 5 Agent Traces
2,380 TRAJECTORIES · 12,490 TRAINING ROWS · 14 MB PARQUET · 663 MB JSONL
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Behavior-preserving instruction-following, tool-use, and agent trajectories
from Claude Fable 5 (anthropic/claude-fable-5). The category and row-share tables
below describe the actual mix seen during training rather than assuming a
particular task… See the full description on the dataset page: https://huggingface.co/datasets/DSFFGFG456/fable-5-coding-and-debugging-traces.glm-5.2-coding-and-debugging-traces
GLM 5.2 Agent Traces
207 TRAJECTORIES · 1,821 TRAINING ROWS · 1 MB PARQUET · 35 MB JSONL
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Behavior-preserving instruction-following, tool-use, and agent trajectories
from GLM 5.2 (glm-5.2). The category and row-share tables
below describe the actual mix seen during training rather than assuming a
particular task domain.
This is an actively growing… See the full description on the dataset page: https://huggingface.co/datasets/greghavens/glm-5.2-coding-and-debugging-traces.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.agentic-coding-trajectories
agentic-coding-trajectories
A unified, tokenized corpus of 15,000 multi-turn agentic-coding sessions (618K turns, 41 turns/session avg) drawn from three publicly-released upstream datasets. Built for benchmarking LLM serving systems on realistic multi-turn coding-agent workloads.
Why this exists
Most LLM serving benchmarks use single-shot prompts. Real coding agents work in long multi-turn loops where each turn appends to a growing prompt. This corpus captures that shape… See the full description on the dataset page: https://huggingface.co/datasets/thoughtworks/agentic-coding-trajectories.transformers-coding-session-captures
dacorvo/transformers-coding-session-captures
HTTP captures of agent ↔ model interactions — one parquet row per
/v1/chat/completions call. Produced by
agentcap.
Native session traces for the same runs live in companion datasets
named transformers-coding-session-<agent>-traces. They're all grouped under the
transformers-coding-session Collection
alongside this dataset. Join on run_id.
Loading
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/dacorvo/transformers-coding-session-captures.kimi-k3-coding-and-debugging-traces
Kimi K3 Coding, Tool Use & Instruction Following Traces
697 TRAJECTORIES · 4,890 TRAINING ROWS · 3 MB PARQUET · 89 MB JSONL
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Behavior-preserving instruction-following, tool-use, and agent trajectories
from Kimi K3 (moonshotai/kimi-k3). The category and row-share tables
below describe the actual mix seen during training rather than assuming a… See the full description on the dataset page: https://huggingface.co/datasets/jiajiale9/kimi-k3-coding-and-debugging-traces.appellate-coding-inputfineweb-eduukb_finemapped_coding
UKBB finemapped coding variants
Predictions from all models
Synthetic-JP-EN-Coding-Dataset-801k
Synthetic-JP-EN-Coding-Dataset-801k
Magpieによって作成したコードSFTデータセットであるAratako/Synthetic-JP-EN-Coding-Dataset-Magpie-69kを元に、Evol-Instructのような手法を用いて複数のinstructionとresonseを生成し拡張して作成した、日英混合801262件のコードSFT用合成データセットです。
日本語: 173849件
英語: 627413件
元のinstructionの作成に利用したモデルは以下の通りです。modelキーに該当レコードの作成に利用したモデル情報があります。
nvidia/Nemotron-4-340B-Instruct
microsoft/Phi-3-medium-4k-instruct
mistralai/Mixtral-8x22B-Instruct-v0.1… See the full description on the dataset page: https://huggingface.co/datasets/Aratako/Synthetic-JP-EN-Coding-Dataset-801k.kimi-k3-coding-and-debugging-traces
Kimi K3 Coding, Tool Use & Instruction Following Traces
582 TRAJECTORIES · 3,956 TRAINING ROWS · 3 MB PARQUET · 72 MB JSONL
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Behavior-preserving instruction-following, tool-use, and agent trajectories
from Kimi K3 (moonshotai/kimi-k3). The category and row-share tables
below describe the actual mix seen during training rather than assuming a… See the full description on the dataset page: https://huggingface.co/datasets/11-47/kimi-k3-coding-and-debugging-traces.glm-5.2-coding-and-debugging-traces
GLM 5.2 Agent Traces
207 TRAJECTORIES · 1,821 TRAINING ROWS · 1 MB PARQUET · 35 MB JSONL
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Behavior-preserving instruction-following, tool-use, and agent trajectories
from GLM 5.2 (glm-5.2). The category and row-share tables
below describe the actual mix seen during training rather than assuming a
particular task domain.
This is an actively growing… See the full description on the dataset page: https://huggingface.co/datasets/ArkhAngelLifeJiggy/glm-5.2-coding-and-debugging-traces.fable-5-coding-and-debugging-traces
Claude Fable 5 Agent Traces
2,374 TRAJECTORIES · 12,448 TRAINING ROWS · 14 MB PARQUET · 662 MB JSONL
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Behavior-preserving instruction-following, tool-use, and agent trajectories
from Claude Fable 5 (anthropic/claude-fable-5). The category and row-share tables
below describe the actual mix seen during training rather than assuming a
particular task… See the full description on the dataset page: https://huggingface.co/datasets/moehamid/fable-5-coding-and-debugging-traces.kimi-k3-coding-and-debugging-traces
Kimi K3 Coding, Tool Use & Instruction Following Traces
601 TRAJECTORIES · 4,089 TRAINING ROWS · 3 MB PARQUET · 73 MB JSONL
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Behavior-preserving instruction-following, tool-use, and agent trajectories
from Kimi K3 (moonshotai/kimi-k3). The category and row-share tables
below describe the actual mix seen during training rather than assuming a… See the full description on the dataset page: https://huggingface.co/datasets/moehamid/kimi-k3-coding-and-debugging-traces.fable-5-coding-and-debugging-traces
Claude Fable 5 Agent Traces
2,161 TRAJECTORIES · 11,235 TRAINING ROWS · 11 MB PARQUET · 656 MB JSONL
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Behavior-preserving instruction-following, tool-use, and agent trajectories
from Claude Fable 5 (anthropic/claude-fable-5). The category and row-share tables
below describe the actual mix seen during training rather than assuming a
particular task… See the full description on the dataset page: https://huggingface.co/datasets/siddharth0713/fable-5-coding-and-debugging-traces.glm-5.2-coding-and-debugging-traces
GLM 5.2 Agent Traces
207 TRAJECTORIES · 1,821 TRAINING ROWS · 1 MB PARQUET · 35 MB JSONL
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Behavior-preserving instruction-following, tool-use, and agent trajectories
from GLM 5.2 (glm-5.2). The category and row-share tables
below describe the actual mix seen during training rather than assuming a
particular task domain.
This is an actively growing… See the full description on the dataset page: https://huggingface.co/datasets/11-47/glm-5.2-coding-and-debugging-traces.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/TypeSafeAI/Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection.expressjs-fastapi-coding-challenges
ExpressJS and FastAPI Coding Challenges
Prerequisites and Environment Setup
1. Required Dependencies
To use this dataset effectively for training and evaluation, you'll need the following dependencies installed on your system:
For ExpressJS (JavaScript) challenges:
Node.js (LTS version recommended)
npm (comes with Node.js)
For FastAPI (Python) challenges:
Python 3.10 or higher
pip
ruff (for linting and formatting - recommended)
For all
Huggingface datasets… See the full description on the dataset page: https://huggingface.co/datasets/amaydle/expressjs-fastapi-coding-challenges.kimi-k3-coding-and-debugging-traces
Kimi K3 Coding, Tool Use & Instruction Following Traces
582 TRAJECTORIES · 3,956 TRAINING ROWS · 3 MB PARQUET · 72 MB JSONL
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Behavior-preserving instruction-following, tool-use, and agent trajectories
from Kimi K3 (moonshotai/kimi-k3). The category and row-share tables
below describe the actual mix seen during training rather than assuming a… See the full description on the dataset page: https://huggingface.co/datasets/Distillio/kimi-k3-coding-and-debugging-traces.gpt-5-6-sol-coding-and-debugging-traces
Mirror: greghavens/gpt-5.6-sol-coding-and-debugging-traces
Pinned snapshot / mirror of greghavens/gpt-5.6-sol-coding-and-debugging-traces, re-hosted for PROTISEC
research reproducibility. Redistributed under the upstream license (cc-by-4.0)
with attribution — all credit to the original author.
Original author: greghavens
Source dataset: greghavens/gpt-5.6-sol-coding-and-debugging-traces
License: cc-by-4.0
Family: coding_traces
Mode: stream
Rows cached: 17939
Changes vs… See the full description on the dataset page: https://huggingface.co/datasets/ansulev/gpt-5-6-sol-coding-and-debugging-traces.glm-5.2-coding-and-debugging-traces
GLM 5.2 Agent Traces
207 TRAJECTORIES · 1,821 TRAINING ROWS · 1 MB PARQUET · 35 MB JSONL
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Behavior-preserving instruction-following, tool-use, and agent trajectories
from GLM 5.2 (glm-5.2). The category and row-share tables
below describe the actual mix seen during training rather than assuming a
particular task domain.
This is an actively growing… See the full description on the dataset page: https://huggingface.co/datasets/rashdan1/glm-5.2-coding-and-debugging-traces.cl-coding-inputSI2CA-Training-TrajectoriesDataset Card for SI2CA-Training-Trajectories
[🌐 Website] •
[🤗 Dataset] •
[📜 Paper] •
[🐱 GitHub]
💡 Introduction
This dataset consists of 32,340 coding-agent trajectories generated by Qwen3.5-122B-A10B on the same 10,780 executable Python SWE tasks under the three trajectory-curation settings of Section 4.4 of the paper: standard sampling, full self-judgement, and an efficient discovered strategy found by the recursive self-improvement framework. Each task is… See the full description on the dataset page: https://huggingface.co/datasets/Self-Improving-Coding-Agents/SI2CA-Training-Trajectories.fable-5-coding-and-debugging-traces
Claude Fable 5 Agent Traces
2,161 TRAJECTORIES · 11,235 TRAINING ROWS · 11 MB PARQUET · 656 MB JSONL
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Behavior-preserving instruction-following, tool-use, and agent trajectories
from Claude Fable 5 (anthropic/claude-fable-5). The category and row-share tables
below describe the actual mix seen during training rather than assuming a
particular task… See the full description on the dataset page: https://huggingface.co/datasets/ArkhAngelLifeJiggy/fable-5-coding-and-debugging-traces.GenBench_coding
Task types by split
task_type
train
test
coding_variant
101
8
conservation_reasoning
624
132
counterfactual
598
202
disease_reasoning
523
93
hallucination_detection
627
173
interaction_propagation
651
149
path_traversal
263
151
structural_effect
677
112
Schema
Each item has:
id, task_type, pipeline (coding_variant/noncoding_regulatory), difficulty
question, answer, choices (MCQ options, when applicable)
context -- either a templated… See the full description on the dataset page: https://huggingface.co/datasets/iit-patna-cse-ai/GenBench_coding.magpie-python-coding-instruction-62k-qwen2.5-bakeneko-32b-instruct
magpie-python-coding-instruction-62k-qwen2.5-bakeneko-32b-instruct
rinna/qwen2.5-bakeneko-32b-instructを用いたMagpieで生成した合成Instructionデータセットです。
なお、計算リソースの問題からoutputの品質評価は行っていません。
ご利用の際はご注意ください。
作成手順
rinna/qwen2.5-bakeneko-32b-instruct-awqを用いたMagpieで"instruction"を生成（magpie_systemの値をシステムプロンプトとして使用）
rinna/qwen2.5-bakeneko-32b-instruct-awqを用いて"instruction"の言語、タスクの種類、難易度、品質を評価
languageがja以外、もしくは品質がpoor/very poorのレコードを削除
rinna/qwen2.5-bakeneko-32b-instruct-awqを用いて応答を"output"として生成
kimi-k3-coding-and-debugging-traces
Mirror: greghavens/kimi-k3-coding-and-debugging-traces
Pinned snapshot / mirror of greghavens/kimi-k3-coding-and-debugging-traces, re-hosted for PROTISEC
research reproducibility. Redistributed under the upstream license (cc-by-4.0)
with attribution — all credit to the original author.
Original author: greghavens
Source dataset: greghavens/kimi-k3-coding-and-debugging-traces
License: cc-by-4.0
Family: coding_traces
Mode: stream
Rows cached: 4607
Changes vs upstream: cached… See the full description on the dataset page: https://huggingface.co/datasets/ansulev/kimi-k3-coding-and-debugging-traces.full_coding_sampling_xml_fitered