datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
coding
Dataset Card for "livebench/coding"
LiveBench is a benchmark for LLMs designed with test set contamination and objective evaluation in mind. It has the following properties:
LiveBench is designed to limit potential contamination by releasing new questions monthly, as well as having questions based on recently-released datasets, arXiv papers, news articles, and IMDb movie synopses.
Each question has verifiable, objective ground-truth answers, allowing hard questions to be scored… See the full description on the dataset page: https://huggingface.co/datasets/livebench/coding.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.llama-coding-agent-evalsOmni-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.verifiable-coding-problems
SYNTHETIC-1
This is a subset of the task data used to construct SYNTHETIC-1. You can find the full collection here
verifiable-coding-problems-python
Dataset Card for Verifiable Coding Problems Python 10k
This dataset contains all Python problems from PrimeIntellect's verifiable-coding-problems dataset. We have formatted the verification_info and metadata columns to be proper dictionaries, but otherwise the data is the same. Please see their dataset for more details.
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.S10-Citadel-Core
Run and deploy your AI Studio app
This contains everything you need to run your app locally.
Run Locally
Prerequisites: Node.js
Install dependencies:
npm install
Set the GEMINI_API_KEY in .env.local to your Gemini API key
Run the app:
npm run dev
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.Nemotron-RL-coding-competitive_coding
Dataset Description:
The Nemotron-RL-coding-competitive_coding dataset is a python-only, reasoning-based, synthetic dataset. It contains competitive coding style problems and their unit test cases. These questions and test cases are collected from CodeContests (deepmind/code_contests), and Open-R1 (open-r1/codeforces)
.
This dataset is released as part of NVIDIA NeMo Gym, a framework for building reinforcement learning environments to train large language models. NeMo Gym contains a… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-coding-competitive_coding.starcoder-python-instruct
StarCoder-Python-Qwen-Instruct
Dataset Description
This dataset contains Python code samples paired with synthetically generated natural language instructions. It is designed for supervised fine-tuning of language models for code generation tasks. The dataset is derived from the Python subset of the bigcode/starcoderdata corpus, and the instructional text for each code sample was generated using the Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8 model.
Creation… See the full description on the dataset page: https://huggingface.co/datasets/OLMo-Coding/starcoder-python-instruct.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.Coding-Conversational-Dataset-Indicgerman-wikipedia-articlesswe-bench-coding-tasks
SWE-Bench Dataset
The dataset comprises 8,712 files across 6 programming languages, featuring verified tasks and benchmarks for evaluating coding agents and language models. It introduces new benchmarks with real-world coding tasks, providing datasets for software engineering problems and tests. It builds upon the original swe-bench by evaluating repository-level challenges and scoring performances.
By utilizing this dataset with its multi-language test sets and golden patches… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/swe-bench-coding-tasks.verifiable-coding-problems-python_decontaminated-testedglm-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.hf-coding-tools-traces-all
HuggingFace AI Coding Tools — Agent Traces
This dataset rehydrates the benchmark results from
davidkling/hf-coding-tools-dashboard
into the JSONL session format consumed by the
Hugging Face Agent Trace Viewer.
What's inside
31 sessions, one per (tool, model, effort, thinking) configuration
9,603 query → response turns total (≈19,206 events)
Tools covered: claude_code, codex, copilot, cursor
Models: claude-opus-4-6, claude-sonnet-4-6, claude-sonnet-4.6, composer-2… See the full description on the dataset page: https://huggingface.co/datasets/davidkling/hf-coding-tools-traces-all.transformers-coding-session-pi-traces
dacorvo/transformers-coding-session-pi-traces
pi coding-agent session traces produced by
agentcap runs. Each run
contributes one folder under data/<run_id>/; inside, one file per
session in pi's native export format.
The on-the-wire HTTP captures for these same runs live in
dacorvo/transformers-coding-session-captures.
Both belong to the
transformers-coding-session Collection
— join on run_id to align captures with traces.
wizardlm8x22b-logical-math-coding-sft
自動生成したテキスト
WizardLM 8x22bで生成した論理・数学・コード系のデータです｡
一部の計算には東京工業大学のスーパーコンピュータTSUBAME4.0を利用しました｡
wizardlm8x22b-logical-math-coding-sft_additional
自動生成したテキスト
WizardLM 8x22bで生成した論理・数学・コード系のデータです｡
一部の計算には東京工業大学のスーパーコンピュータTSUBAME4.0を利用しました｡
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.agentic-coding-trajectories
Agentic Coding Trajectories
Rights & intended use: legacy public research corpus / portfolio
artifact. Hosted frontier-model outputs are research-only inputs under
project policy (synthetic-factory#161):
intended_use: research_only, project_training_policy: blocked. Not
training data for any model-weight update. Machine-readable record:
rights.json.
Release status: The raw, uncurated coding-episode payload is published under
data/raw/. It is available for inspection and… See the full description on the dataset page: https://huggingface.co/datasets/rmems/agentic-coding-trajectories.pi-coding-sessionsOmni-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-tracesThis dataset is aggregated from:
"AletheiaResearch/GLM-5.2-Agent",
"armand0e/claude-fable-5-claude-code",
"armand0e/minimax-m3-claude-code-traces",
"AletheiaResearch/Kimi-K3-Codex"
and has been converted into a single unified format:
{"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "<think>...</think>..."}, ...]}
The original raw files are kept in the .raw_sources folder for reference if you'd like to take a closer look.
Main file: USE_THIS_train.jsonl
ty
ti_coding_agent_training_probe_20260624
Open-SWE-Traces Swift Probe 5K
Balanced 5,000-row training probe subset from nvidia/Open-SWE-Traces, exported for ModelScope SWIFT-style SFT.
Selection:
1,250 hard-filter-kept rows from each source config.
Original native scaffold semantics are preserved.
MiniMax rows are exported as thinking examples by wrapping reasoning_content in <think>...</think>.
Qwen rows are exported as non-thinking examples; reasoning_content is not emitted.
Tool responses are included as role: "tool"… See the full description on the dataset page: https://huggingface.co/datasets/eigentom/ti_coding_agent_training_probe_20260624.ox-alpha-glm-5.3-flash-distillation-coding-17k-raw
Ox Alpha GLM-5.3-Flash Distillation Coding 17K Raw
A raw collection of 17,138 synthetic coding samples generated with GLM-5.3-Flash, previously exposed through OpenCode under the stealth-model alias Ox Alpha.
The dataset is intended for experimentation with LLM distillation, code-generation models, instruction tuning, supervised fine-tuning, evaluation, and agentic coding systems.
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.hf-coding-tools-traces_april26
HuggingFace AI Coding Tools — Agent Traces
This dataset rehydrates the benchmark results from
davidkling/hf-coding-tools-dashboard
into the JSONL session format consumed by the
Hugging Face Agent Trace Viewer.
What's inside
32 sessions, one per (tool, model, effort, thinking) configuration
9,130 query → response turns total (≈18,260 events)
Tools covered: claude_code, codex, copilot, cursor
Models: claude-opus-4-6, claude-sonnet-4-6, claude-sonnet-4.6, composer-2… See the full description on the dataset page: https://huggingface.co/datasets/clem/hf-coding-tools-traces_april26.