datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
inference-datagroot_n1.7_inference_on_diff_data
GROOT Inference Analysis Log
Evaluation records for a GR00T policy trained on the task
"pick octopus and place inside brown basket", run on a Unitree G1 at 20 Hz
with the ego_view stereo camera.
Six training checkpoints (50, 100, 150, 200, 250, 300 demonstration episodes) were each
evaluated on 50 inference episodes. Every episode is recorded here with video,
per-tick state/action logs and run metadata.
Success rate
Checkpoint (training episodes)
Success… See the full description on the dataset page: https://huggingface.co/datasets/rahul-ai-01/groot_n1.7_inference_on_diff_data.code-world-model-inference-examples-40
Inference examples
This directory contains 40 numbered, independent inference examples.
Every example uses only its public number; source case names and internal paths
are intentionally omitted.
Each numbered directory contains:
first_frame.png: exact 1536x864 generated RGB first frame used by inference.
prompts/*.txt: the exact rolling long-inference prompts used for the result.
condition/*.npz: ordered lossless condition chunks.
metadata.json: frame count, FPS, prompt windows… See the full description on the dataset page: https://huggingface.co/datasets/NTU-yiwen/code-world-model-inference-examples-40.inference-scratchdata_inference_pythia_6_9binference-dataset
VLM-Gym Inference Dataset
This dataset contains pre-defined test episodes and initial states for evaluating Vision-Language Models (VLMs) on the VLM-Gym benchmark.
Dataset Structure
inference-dataset/
├── test_set_easy/ # Easy difficulty test episodes (JSONL)
├── test_set_hard/ # Hard difficulty test episodes (JSONL)
├── initial_states_easy/ # Initial environment states for easy episodes (JSON)
├── initial_states_hard/ # Initial environment… See the full description on the dataset page: https://huggingface.co/datasets/VisGym/inference-dataset.p2-etf-active-inference-resultshf-inference-providers-datatamil-inference-resultInference_Step1XEditQwen-SFT-Inference-Outputsmodel-inference-activationsspeculators-ci-datasets
speculator-tutorial
Raw vs. on-policy regenerated conversation data for training speculative-decoding
drafters (EAGLE-3 / DFlash / DSpark style), with the original source data kept alongside
so you can see exactly what regeneration changes and why it matters.
Prompts come from UltraChat-200k. The verifier / teacher model is Qwen/Qwen3-8B.
Why regenerate at all?
A speculative-decoding drafter is trained to predict what the verifier would say next.
If you train it… See the full description on the dataset page: https://huggingface.co/datasets/inference-optimization/speculators-ci-datasets.inference-benchmarkerfast-autoregressive-inference-gp-trainK4eval_pi0_inference_only_datasetThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "so101_follower",
"total_episodes": 70,
"total_frames": 40583,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 500,
"fps": 30,
"splits": {
"train": "0:70"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/tersooawai/eval_pi0_inference_only_dataset.qwen3.8-max-glm5.2-kimi-k3-distillation
Multi-Teacher Distillation Dataset (57,937 traces)
A quality-filtered, deduplicated, multi-teacher SFT corpus combining traces from three frontier models across math, code, reasoning, instruction-following, tool-use, science, long-context, multilingual, and creative dialogue domains.
Teachers
Teacher
Provider
Traces
Qwen3.8-Max-Preview
Alibaba Cloud Model Studio
48,283
GLM-5.2
Z.AI Coding Plan
5,307
Kimi Code K3
Moonshot AI (Kimi)
4,347… See the full description on the dataset page: https://huggingface.co/datasets/inferenceport-ai/qwen3.8-max-glm5.2-kimi-k3-distillation.sovereign-shadow-inference-bench
Sovereign Shadow Inference Bench
A public, versioned evidence surface for independent Hugging Face shadow inference beside Sovereign's primary OpenRouter/Revolver route.
What this dataset proves
The seed record in data/shadow_receipts.jsonl was produced by one real Hugging Face Inference Providers request. It records provider/model identity, request bounds, latency, hashes, literal-match outcome, source revision, and an immutable receipt hash.
What it… See the full description on the dataset page: https://huggingface.co/datasets/Thorsu/sovereign-shadow-inference-bench.model-inference-responsesinference-test-settfds_aloha_game_inference_11_20amortized-inference-training
Amortized Inference Training Trajectories
Generated trajectories are organized as source_dataset/geometry_id/material_id/trajectory.
Each geometry has index.jsonl; the global decisions live under index/.
Only records with status == "accepted" and trainable == true passed the recorded legacy
topology gate. rejected arrays are intentionally absent. Historical trajectories for which the
topology gate was never run are retained as unreviewed and must not be used for training until… See the full description on the dataset page: https://huggingface.co/datasets/BDXXN/amortized-inference-training.RoboTryOn_inferencetfds_aloha_game_inference_256_11_15vizdoom-inference-latent-datasetAs for the details of this dataset, please refer to https://github.com/Masao-Taketani/GameNGen.
llava_qwen3vl_sae_inference_featuresVILA-inference-demoscamus-17-configurable-inference
We externalize 10 inference parameters to a JSON config file — hot-reloadable settings for diverse hardware.
Configurable Inference Settings via File-Based Configuration
The Problem
Hard-coded inference parameters make the system inflexible. Different hardware and tasks need different settings.
What We Built
JSON-based configuration file (camus.json) storing all user-settable parameters: n_ctx, n_threads, temperature, top_p, max_tokens, show_bars… See the full description on the dataset page: https://huggingface.co/datasets/Anticloud/camus-17-configurable-inference.InferenceNetInferenceNet project portal · Home · Data overview · Leaderboard · Agent / Harness
Explore the project and its published research results in the linked Space. This dataset repository remains the source for the task lists and research data.
InferenceNet: Data Card for Econometric AI Agent Testset
InferenceNet is a project aimed at evaluating and building up the AI capability for social science research related to empirical studies. We collect the world’s largest dataset on… See the full description on the dataset page: https://huggingface.co/datasets/CamoAiLab/InferenceNet.camus-17-configurable-inference
We externalize 10 inference parameters to a JSON config file — hot-reloadable settings for diverse hardware.
Configurable Inference Settings via File-Based Configuration
The Problem
Hard-coded inference parameters make the system inflexible. Different hardware and tasks need different settings.
What We Built
JSON-based configuration file (camus.json) storing all user-settable parameters: n_ctx, n_threads, temperature, top_p, max_tokens, show_bars… See the full description on the dataset page: https://huggingface.co/datasets/kleinnner/camus-17-configurable-inference.