datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
harbor-swesmith-rl-artifacts
Harbor SWE-Smith 强化学习数据产物
本数据集是 Harbor Qwen 工具调用代码智能体强化学习项目使用的冻结任务集，服务于 GRPO、原生价值模型/GAE PPO、训练过程诊断和统一协议评测。
项目已于 2026 年 8 月 30 日完成 P0 评测并进入阶段性归档。本数据集用于保留实验所依赖的数据切分、任务执行文件和审计信息，不代表新的通用代码能力基准。
数据概况
切分
任务数
训练集
187
验证集
42
测试集
38
合计
267
数据覆盖 89 个上游代码仓库。三个切分之间同时执行任务标识和仓库级隔离检查。
正式数据集名称：
swesmith-curated-grpo-267-v1
冻结切分的语义摘要：
ae5df9a3f4a3fc8af44fac420b36529e283839e1bd3de9daba65d5bcda51447d
该值来自 split-manifest.json 的 sha256 字段，用于标识切分语义，不等同于该文件本身的字节级… See the full description on the dataset page: https://huggingface.co/datasets/keryszhan/harbor-swesmith-rl-artifacts.tau2-uq-artifacts
tau2-bench UQ Artifacts
Interaction trajectories and token-level log-probability measurements from conversational customer service agent evaluations on tau2-bench, collected as part of the uncertainty quantification (UQ) pipeline. Used for analyses in the paper "Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities" under the agentuq codebase.
Dataset Overview
This dataset contains two types of artifacts:
Trajectories --… See the full description on the dataset page: https://huggingface.co/datasets/changdae/tau2-uq-artifacts.dsv4-eval-artifacts
DeepSeek-V4-Flash-0731 — quantization measurements
Everything needed to reproduce, audit or extend the numbers published in
AtomicChat/DeepSeek-V4-Flash-0731-GGUF:
the reference logits, the evaluation corpus, the raw tool output for every quant we
measured, and the parsed results.
Every GGUF of this model that we could find on the Hub was measured here — ours,
unsloth's, bartowski's, ggml-org's, antirez's and others — on one machine, against one
reference, with one command.… See the full description on the dataset page: https://huggingface.co/datasets/AtomicChat/dsv4-eval-artifacts.patchaudit-artifact
PatchAudit Artifact
PatchAudit audits security patches. You give it a CVE's initial fix — commit C1 — and a later commit Ci,
and it tells you whether Ci is a future commit: a later commit that had to keep fixing the same problem
because C1 was incomplete (it left the vulnerability reachable) or incorrect (its own change
introduced a new defect). When such a future commit exists, C1 was a bad patch. When even the latest fix
still leaves the hole open, the bug is a lingering… See the full description on the dataset page: https://huggingface.co/datasets/zhcharyzhang/patchaudit-artifact.public-agent-coordination-artifacts
Public Agent Coordination Artifacts
Real, complete edits and posts that AI agents left on public wikis and paste sites —
collected as open evidence for studying how autonomous agents use shared online spaces to
remember things, signal each other, and coordinate. It's the behavior spotlighted by the
mid-2026 OpenAI–Hugging Face agent incident,
here as raw public data researchers can actually inspect — plus a small, hand-reviewed map
of how specific artifacts relate.… See the full description on the dataset page: https://huggingface.co/datasets/leonidas1712/public-agent-coordination-artifacts.artifact-bench
ArtifactBench
A heterogeneous graph of HuggingFace model / dataset / paper / codebase
nodes (14,053) with observed (model, dataset, performance-metric)
evaluation edges (51,337 relations), for benchmarking link prediction and
attribute (metric-value) regression, plus an agent-based verification suite.
License
Released under the Open Database License (ODbL) v1.0 — see LICENSE or
https://opendatacommons.org/licenses/odbl/1-0/. Share/modify/use freely
with… See the full description on the dataset page: https://huggingface.co/datasets/lwaekfjlk/artifact-bench.ArtifactsBenchmark
ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluation
Tencent Hunyuan Team
📖 Paper •
🏠 Home Page •
💻 Code •
🏆 Leaderboard •
📜 Citation
Figure 1: Automation level versus human–alignment across evaluation frameworks. The red star marks the fully manual WebDev Arena (100% human effort), while the blue bubble denotes our checklist-guided MLLM evaluation, ArtifactsBench, which achieves 94.4% agreement with human… See the full description on the dataset page: https://huggingface.co/datasets/tencent/ArtifactsBenchmark.backup-drmas-noshare-8b-historical-run-artifacts
Historical DrMAS Noshare 8B run artifacts
Public backup of the historical drmas-checklist-bs16-n4-c1-agent Qwen3-8B run. This was a Noshare topology with separately trained Tool Caller and Tool Simulator agents (world_size=8).
Contents
rollout_dumps/: 470 training rollout JSONL files
val_dumps/: 47 validation JSONL files
Four training logs
latest_checkpointed_iteration.txt
522 business files and 5,189,359,695 logical bytes in total
Model scope
The… See the full description on the dataset page: https://huggingface.co/datasets/xuzishan/backup-drmas-noshare-8b-historical-run-artifacts.crooked-nebula-artifactspostdyn-artifactsvqa-cmsv-benchmark
VQA-CMSV Benchmark Data Package
This repository contains annotation splits for VQA v2-CMSV, GQA-CMSV, and VG-CMSV, plus patch-mask NPZ files used for mask supervision experiments.
Contents
data/vqa_v2_cmsv/train.json, data/vqa_v2_cmsv/val.json, data/vqa_v2_cmsv/test.json
data/gqa_cmsv/train.jsonl, data/gqa_cmsv/val.jsonl, data/gqa_cmsv/test.jsonl
data/vg_cmsv/train.jsonl, data/vg_cmsv/val.jsonl, data/vg_cmsv/test.jsonl
masks/vqa_v2_cmsv_masks.npz
masks/gqa_cmsv_masks.npz… See the full description on the dataset page: https://huggingface.co/datasets/as-benchmark-artifacts/vqa-cmsv-benchmark.grok-1-dissect-artifacts
Grok-1 structural parse artifacts (personal research)
Personal research tooling output — not a product release and not a hybrid-quantization
implementation. Structural parse of open Grok-1 checkpoint shards (tensor inventory, MoE
expert layout, routing-critical tensors, conversion policies, reports) produced by the
rmems/xai-dissect weight parser.
This is not a copy of the model weights, not a finetune, not an inference
runtime, and does not implement hybrid quantization.… See the full description on the dataset page: https://huggingface.co/datasets/rmems/grok-1-dissect-artifacts.tr-rl-eval-artifactsbackup-drmas-caller-only-8b-historical-run-artifacts
Historical DrMAS Caller-only 8B run artifacts
Public backup of the historical drmas-checklist-bs16-n4-c1-httpx-caller_only Qwen3-8B run. The Tool Caller was trained while the Tool Simulator used an external frozen Qwen3-8B service (world_size=8).
Contents
rollout_dumps/: 470 training rollout JSONL files
val_dumps/: 47 validation JSONL files
latest_checkpointed_iteration.txt
518 business files and 945,040,414 logical bytes in total
Model scope
The… See the full description on the dataset page: https://huggingface.co/datasets/xuzishan/backup-drmas-caller-only-8b-historical-run-artifacts.stillwarm-kv-cache-artifact
A downloadable KV-cache save file — with the honest math
One llama-server slot save: the first 8,192 Llama-tokens of Frankenstein
(public domain), prefilled by Qwen2.5-7B-Instruct Q4_K_M (Apache-2.0 model —
chosen over Llama specifically for artifact licensing) and saved with a
stillwarm sidecar.
This file is USELESS unless your setup matches the sidecar exactly:
field
value
llama.cpp build
b9871 (ef2d770117db45b05aa7ecd1b0acca36370c5470) — advisory: ±5 weeks measured… See the full description on the dataset page: https://huggingface.co/datasets/vimalnakrani/stillwarm-kv-cache-artifact.persona-artifactsfunding-extraction-artifact-data-mix-grpo-mixed-reward
Funding Extraction Training Data
Training, evaluation, and test data for fine-tuning LLMs to extract structured funding metadata (funder names, award IDs, funding schemes, award titles) from academic paper funding statements.
Dataset Structure
data/
├── full/ # Complete unsplit dataset
│ ├── train.jsonl # 5,264 real Crossref funding statements
│ └── synthetic.jsonl # 10,124 LLM-generated funding statements
├── sft/… See the full description on the dataset page: https://huggingface.co/datasets/cometadata/funding-extraction-artifact-data-mix-grpo-mixed-reward.swiss-caselaw-artifactsculture-mt-repro-artifacts
CULTURE-MT Reproduction Artifacts — ICML 2026 #33401
Reproduction artifacts for "Beyond Literal Translation: Evaluating Cultural Effectiveness in Social Media UGC" (OpenReview 7xppFNbcXM, arXiv 2605.25626), produced for the ICML-2026 Agent Repro challenge.
📓 Logbook (full write-up, claim by claim): https://huggingface.co/spaces/Manaiakalani/culture-mt-repro
Contents
Path
What
data/CULTURE-MT_test.jsonl
1,002 source Chinese UGC notes (from… See the full description on the dataset page: https://huggingface.co/datasets/Manaiakalani/culture-mt-repro-artifacts.soundvector-artifactsknowledge-graph-rag-retrieval-artifacts
Knowledge Graph RAG Retrieval Artifacts
Pinned vector-retrieval artifacts for the Knowledge Graph RAG Assistant, a Washington State University capstone project combining knowledge-graph and dense-vector retrieval.
This repository is a project-owned, documented mirror of the two binary artifacts used by the maintained application. The files are byte-identical to the current artifacts originally hosted in miverson9/acme10-he-ragapp-embeddings at revision… See the full description on the dataset page: https://huggingface.co/datasets/ethanvillalovoz/knowledge-graph-rag-retrieval-artifacts.vscode-issue-rag-artifactsmind-artifactsnyc-restaurant-artifactsmicroduck-video-artifactsegret-artifactsneurips-2025-paperswithcode-artifactsINFUSER-Qwen3-8B-base-artifacts
INFUSER Qwen3-8B-base Artifacts
This dataset contains raw INFUSER Qwen3-8B-base generator output artifacts for steps 0, 30, 60, and 90, as presented in the paper INFUSER: Influence-Guided Self-Evolution Improves Reasoning.
Paper | Code | Blog
Artifact Information
Source run path: qwen3_8b_base/FW-Alr_2e-6-Glr_4e-6-DrGRPO-TIS_token-dev_800-precond_cos-seed456
Source model/run context: INFUSER Qwen3-8B-base generator outputs from training config… See the full description on the dataset page: https://huggingface.co/datasets/Siyuc/INFUSER-Qwen3-8B-base-artifacts.swiss-caselaw-artifactsoqkiE71wrC-artifacts