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.real-pi-coding-agent-traces-sessions
Real Pi Coding Agent Traces Sessions
An aggregated dataset of real human–AI coding agent sessions, collected from 21 independently published Hugging Face datasets and hand-filtered to exclude synthetic or AI-generated content.
Every session is an unedited (but redacted) trace of a real person using pi — an open-source AI coding agent harness — to build, debug, and ship real open-source software. Real prompts, real tool calls, real errors, real backtracking.
Why this… See the full description on the dataset page: https://huggingface.co/datasets/MaxDevv/real-pi-coding-agent-traces-sessions.Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection
🌌 Omni-Frontier Distillation SFT
The Definitive Evolution of Open-Source Distillation & Human-Crafted Expertise
Repository: Manusagents/Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection
"The most comprehensive multi‑domain SFT corpus ever assembled — fusing 6.86 million cleaned distillation samples with 9.14 million human‑crafted expert examples across medical, cybersecurity, chemical, robotics, humanities, and more. 16 million… See the full description on the dataset page: https://huggingface.co/datasets/Manusagents/Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection.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.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.gpt-5.6-sol-coding-and-debugging-traces
GPT-5.6 Sol Coding & Debugging Traces
Verified software-engineering, independent model-judging, seed-authoring,
defensive-security, and training-harness trajectories from
GPT-5.6 Sol (gpt-5.6-sol) running through the Codex CLI as an
autonomous coding agent. Sessions show the observable development loop:
inspecting repositories, reproducing failures, explaining evidence, editing
files, running compilers and test suites, correcting mistakes, and verifying
the completed result.… See the full description on the dataset page: https://huggingface.co/datasets/greghavens/gpt-5.6-sol-coding-and-debugging-traces.Nemotron-RL-coding-quality-filtered
Nemotron coding — quality pool, revision 2
8,201 retained tasks from 16,083 upstream train rows (51.0%).
This is a static quality screen for Python standard-input/standard-output coding RL. It is not a reference-verified gold dataset.
All difficulty levels are eligible. There is no model-accuracy filter, rollout generation, rating cutoff, random subsampling, or 3,200-row cap.
Source: NVIDIA Nemotron-RL-coding-competitive_coding, revision 755d5910fc8646b385e3926eec08c152051cdc07.… See the full description on the dataset page: https://huggingface.co/datasets/hi-todayis-jh/Nemotron-RL-coding-quality-filtered.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.Nemotron-LeetCode-coding-clean-3.2k
Nemotron + LeetCode Coding Clean 3.2k
3,200 distinct training problems, seed 42, intended for Python coding reinforcement learning. This is a training mix, not a held-out benchmark. It combines the pinned default train Parquet split of Nemotron-RL-coding-competitive_coding with the train JSONL of LeetCodeDataset.
Composition
Source
Questions
Selection
Nemotron / Codeforces
1,856
1000–1600, inclusive
Nemotron / AtCoder
366
300–2000 display difficulty… See the full description on the dataset page: https://huggingface.co/datasets/hi-todayis-jh/Nemotron-LeetCode-coding-clean-3.2k.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-Chain-of-Thought-Coding-SFT-Dataset
🤖 Agentic Coding CoT Dataset
A high-quality supervised fine-tuning (SFT) dataset for training agentic coding assistants with Chain-of-Thought reasoning capabilities.
📋 Dataset Description
This dataset was created by processing and distilling ~20GB of GitHub crawl data using Minimax-M2 to generate structured, reasoning-rich coding examples. Each sample demonstrates systematic problem-solving with explicit tool usage patterns.
🏗️ Assistant Data Structure… See the full description on the dataset page: https://huggingface.co/datasets/AlicanKiraz0/Agentic-Chain-of-Thought-Coding-SFT-Dataset.SI2CA-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.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.guidellm-agentic-coding-trajectories
GuideLLM agentic coding trajectories
A sampled serving-load benchmark derived from Thoughtworks agentic-coding-trajectories, for GuideLLM and an OpenAI-compatible /v1/chat/completions endpoint. There are 630 rows representing 481 unique source sessions, across the same 8turn, 24turn, and 48turn configurations as the earlier version.
The configuration names now refer to original logical steps, not always HTTP request counts. Native tool steps expand into a tool-call request and a… See the full description on the dataset page: https://huggingface.co/datasets/zetomatoz/guidellm-agentic-coding-trajectories.Roblox-luau-coding_L1
8BitStudio/Roblox-luau-coding_L1
A dataset for training and fine-tuning AI models on Roblox Luau scripting.
Covers a wide range of scripting topics from beginner to advanced.
Summary
This dataset contains 12,306 Luau code examples designed to teach AI models
how to write scripts for Roblox. Topics range from basic part manipulation
to complex datastore systems.
Dataset Structure
Data Format
Each example is a tab-separated pair of a… See the full description on the dataset page: https://huggingface.co/datasets/8BitStudio/Roblox-luau-coding_L1.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.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.dataclaw-peteromallet
Claude Code Conversation Logs
Exported with DataClaw.
Tag: dataclaw — Browse all DataClaw datasets
Stats
Metric
Value
Sessions
549
Projects
14
Input tokens
15.1B
Output tokens
4.6M
Last updated
2026-02-24
Models
Model
Sessions
claude-opus-4-6
270
claude-opus-4-5-20251101
256
claude-sonnet-4-6
16
claude-haiku-4-5-20251001
6
claude-sonnet-4-5-20250929
1
Schema
Each line in conversations.jsonl is one… See the full description on the dataset page: https://huggingface.co/datasets/Codingxx/dataclaw-peteromallet.Paragon-coding
NOTICE
This was done by me, someone with a learning Disability. So please do bare with me when updating this with more working data.
Multi-Language Programming Code Dataset
A curated dataset of original, non-scraped code examples across 7 programming
environments: Python, JavaScript, Node.js, Java, C, C++, and Rust.
The dataset ships in two parts that can be used separately or combined:
File
Rows
Description
code_dataset.jsonl / .csv
105
Hand-written… See the full description on the dataset page: https://huggingface.co/datasets/TGPRO32/Paragon-coding.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.fable5-agentic-coding-sft
FABLE.5 Agentic Coding SFT (curated)
~159,972 supervised fine-tuning examples for agentic coding — multi-turn conversations where the
assistant drives a tool-call loop (shell, file edits, tests) and commits to complete solutions. Used to train
VibeThinker-Fable-Nano-Agentic-3B.
Provenance & license
Curated/distilled from the Complete-FABLE.5-traces-2M trace set:
Original source: Glint-Research/Complete-FABLE.5-traces-2M (currently gated).
Pulled from:… See the full description on the dataset page: https://huggingface.co/datasets/Nexlab/fable5-agentic-coding-sft.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.tool-reasoning-sft-CODING-text_to_terminal_v2-sft-tool-use-agent-data-cleaned-rectified
Text to Terminal, v2 — Cleaned & Rectified
👥 Follow the Author
Aman Priyanshu
Overview
This dataset is a cleaned, combined, and thinking-augmented version of muellerzr/text_to_terminal_v2. It pairs natural language instructions with their corresponding terminal/bash commands, now augmented with explicit <think> reasoning traces that model the step-by-step thought process before producing the final command.The restructuring approach is directly… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-CODING-text_to_terminal_v2-sft-tool-use-agent-data-cleaned-rectified.Kimi-K2.7-CodingTraces-9000x
Kimi K2.7 Coding Traces 9000x
A validated 9,014-row coding and software-engineering reasoning
dataset generated with moonshotai/Kimi-K2.7-Code.
Every row contains a coding-focused prompt, a separated reasoning trace, and a
final answer. The release was built from a durable Google Drive generation
pipeline and underwent a complete two-pass schema and delimiter audit before
publication.
Generation configuration
Setting
Value
Teacher… See the full description on the dataset page: https://huggingface.co/datasets/trjxter/Kimi-K2.7-CodingTraces-9000x.fable-5-coding-and-debugging-traces-synthetic-corrections
Model Synthetic Corrections
1 TRAJECTORIES · 2 TRAINING ROWS · 16 kB
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Synthetic Corrections companion dataset. The original dataset is greghavens/fable-5-coding-and-debugging-traces. These are narrowly, synthetically corrected, independently re-judged traces that never passed in the original dataset.
Behavior-preserving instruction-following… See the full description on the dataset page: https://huggingface.co/datasets/greghavens/fable-5-coding-and-debugging-traces-synthetic-corrections.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.swe-bench-coding-tasks
SWE-Bench Dataset - 8,712 files
The dataset comprises 8,712 files across 6 programming languages, featuring verified tasks and benchmarks for evaluating coding agents and language models. It supports coding agents, language models, and developer tools with verified benchmark scores and multi-language test sets. - Get the data
Dataset characteristics:
Characteristic
Data
Description
An extended benchmark of real-world software engineering tasks with enhanced… See the full description on the dataset page: https://huggingface.co/datasets/ud-nlp/swe-bench-coding-tasks.swe-coding-instruction-following
SWE Coding Instruction-Following
A curated collection of real-world software engineering tasks in the SWE-bench format, designed for evaluating instruction-following capabilities of coding agents. Each task represents a genuine GitHub issue with a reproducible environment, test suite, and reference solution — the agent must precisely follow the issue instructions to produce a correct fix.
Overview
Item
Details
Total Tasks
50
Repositories
2 (pallets/click… See the full description on the dataset page: https://huggingface.co/datasets/obaydata/swe-coding-instruction-following.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.Vibe-Coding-Instruct