datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Gradients_Gradients_and_Text_Full_Logic_CaptionsSSR-3DFRONT
SSR-3DFRONT: Structured Scene Representation for 3D Indoor Scenes
This dataset provides a processed version of the 3D-FRONT dataset with structured scene representations for text-driven 3D indoor scene synthesis and editing.
Mor information about ReSpace: http://respace.mnbucher.com
For detailed usage instructions, training details, and examples, see the associated repository: https://github.com/GradientSpaces/respace
Our model weights for SG-LLM:… See the full description on the dataset page: https://huggingface.co/datasets/gradient-spaces/SSR-3DFRONT.fineweb_synth_dense_ocr_gradients_with_grounding_Bfineweb_synth_dense_ocr_gradients_with_grounding_AWildChat-4.8M-filtered-gpt-4SWE-ZERO-12M-trajectories-filtered
Filtered SWE Trajectories — 60K
This dataset contains 60,000 software-engineering agent trajectories from two
upstream datasets:
50,000 Submitted trajectories sampled from
AlienKevin/SWE-ZERO-12M-trajectories
10,000 accepted trajectories sampled from
Kwai-Klear/SWE-smith-mini_swe_agent_plus-trajectories-66k
All rows use this common schema:
instance_id
repo
messages
trajectory_format
exit_status
duration_sec
For the SWE-smith rows, repo was derived from the source instance_id… See the full description on the dataset page: https://huggingface.co/datasets/gradients-io-tournaments/SWE-ZERO-12M-trajectories-filtered.code-gradients
Gradients code comparison
This public copy contains 18,718 training rows and 998 test rows. Each row has
exactly one user turn followed by one assistant turn under conversations. The local
source uses the equivalent top-level key messages; only that key was renamed for
compatibility with Gradients baseline preparation.
pvp-tool-calling-sft
PvP tool-calling SFT cold-start data
Claude-vs-Claude games played through the G.O.D PvP tool-calling harness. Each row is one model turn (or post-game reflection): the system+user prompt the harness built, the assistant response (content + tool_calls), and the tools schemas — i.e. the OpenAI messages+tools format consumed by tokenizer.apply_chat_template(messages, tools=tools). On a move turn the assistant co-emits any memory-tool edits and a game_action committing a legal… See the full description on the dataset page: https://huggingface.co/datasets/gradients-io-tournaments/pvp-tool-calling-sft.repro-on-the-theory-of-continual-learning-with-gradient-descent-for-neural-networks-traces
Agent traces
Agent sessions published from a Trackio Logbook.
env_training_gradientsGradient-Decomposition-Assay
Gradient Decomposition Assay (GDA)
This repository contains the CSV corpus and summary tables for the Gradient Decomposition Assay, an exploratory behavioral evaluation of how frontier language models respond to an eight-vector prompt manifold ranging from benign technical tasks to adversarial compression and counterfactual/narrative reframing.
Why this repository uses multiple configurations
The CSV files in this dataset are not all the same table. The row-level… See the full description on the dataset page: https://huggingface.co/datasets/devinendorphin/Gradient-Decomposition-Assay.Gradient-Reasoninggradient-shapes-svgThis is a data set of random shapes with gradients to use for evaluating or training vision language models!
repro-gradmem-learning-to-write-context-into-memory-with-test-time-gradient-descent-traces
Agent traces
Agent sessions published from a Trackio Logbook.
uniformat-datasetdpo-gradients
Gradients DPO comparison
This public copy contains 53,334 training rows and 1,715 test rows. Each row has
the flat string fields prompt, chosen, and rejected expected by Gradients DPO
preparation. It is a lossless field conversion of the local Together-format source:
the user message becomes prompt, the preferred assistant content becomes chosen,
and the non-preferred assistant content becomes rejected.
legal-ner
Dataset Card for Legal NER dataset
Dataset Description
This dataset is based on COMtext.SR.legal, the first corpus of legal-administrative texts in Serbian that has been manually annotated for Named Entities (NER) according to the IOB2 standard.
The corpus was created during 2023. The selection of representative legal texts of various types (contracts, judgments, conclusions, decisions, requests, appeals, rulebooks, laws, decrees, statutes, etc.) was conducted with the… See the full description on the dataset page: https://huggingface.co/datasets/gradientflow/legal-ner.reasoning-improved-gradients
Gradients reasoning comparison
This public copy contains 100,000 training rows and 1,000 test rows. Each row has
exactly one user turn followed by one assistant turn under conversations. The local
source uses the equivalent top-level key messages; only that key was renamed for
compatibility with Gradients baseline preparation.
math-gradients
Gradients math comparison
This public copy contains 7,913 training rows and 879 test rows. Each row has
exactly one user turn followed by one assistant turn under conversations. The local
source uses the equivalent top-level key messages; only that key was renamed for
compatibility with Gradients baseline preparation.
autonomous-driving-minimal-harm-gradient-pathfinding-v0.1
What this dataset tests
Whether a system can navigatea minimal-harm gradient through a driving scene.
The task is to identify the paththat minimizes total deformationacross all agents.
Required outputs
gradient vectors across actions
minimal harm path
deformation score
stability margin
Use case
Second layer of ethical navigation stack.
Transforms ethical cost fieldinto an actionable path.
Evaluation
Predictions must:
describe gradient… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/autonomous-driving-minimal-harm-gradient-pathfinding-v0.1.exp_8_10_knowledge_gradient_expert_test25exp_8_10_knowledge_gradient_intermediate_test25exp_8_10_knowledge_gradient_novice_test5PubMedQA-Testexp_8_10_knowledge_gradient_novice_test25exp_8_10_knowledge_gradient_intermediate_test5gradientai__Llama-3-8B-Instruct-Gradient-1048k-details
Dataset Card for Evaluation run of gradientai/Llama-3-8B-Instruct-Gradient-1048k
Dataset automatically created during the evaluation run of model gradientai/Llama-3-8B-Instruct-Gradient-1048k
The dataset is composed of 44 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 2 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… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/gradientai__Llama-3-8B-Instruct-Gradient-1048k-details.F1-aero-platform-recovery-and-stability-gradient-v0.1What this dataset tests
Whether a system can maphow an aero platform recovers after localized collapseand identify fragile zones that persist.
Focus
Recovery pathstability gradient across zonespersistent imbalancerecovery latencyfragility hotspotsnext collapse risk
Required outputs
recovery path profile
stability gradient map
persistent imbalance flags
recovery latency score
fragility hotspots
next collapse risk score
All indices0 to 1
Higher latency and next riskmean slower… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/F1-aero-platform-recovery-and-stability-gradient-v0.1.ni-unique-20-tasks-gradient-1.0-20250113sdc-gradient-chart-xhard-v1
sdc-gradient-chart-xhard-v1
Gradient chart and heatmap — Extra Hard tier
Dataset Info
Rows: 2
Columns: 3
Columns
Column
Type
Description
image
Image(mode=None, decode=True)
Chart PNG
caption
Value('string')
Description
name
Value('string')
Chart ID
Generation Parameters
{
"script_name": "run_extra_hard_zero_shot.py",
"model": "gpt-5-2",
"description": "Gradient chart and heatmap \u2014 Extra Hard tier",
"experiment_id":… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-degeneration-dev/sdc-gradient-chart-xhard-v1.