datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
eagle
Eagle 🦅: Ethical Dataset Given from Real Interactions
Introduction
This repository contains the Eagle dataset, which is an ethical dataset of real interactions between humans and ChatGPT. This dataset is created to evaluate social bias, opinion bias, toxic language, and morality in Large Language Models (LLMs).
If you use the Eagle dataset in your research, please cite the following:
@inproceedings{Eagle:arxiv:2024,
title={Eagle: Ethical Dataset Given from Real… See the full description on the dataset page: https://huggingface.co/datasets/MasahiroKaneko/eagle.sbucaptionssec-13f-holdings
SEC Form 13F Hedge Fund Holdings
Quarterly US-listed equity holdings for 9 institutional managers,
reconstructed from their own Form 13F-HR filings with the SEC.
Built for the trackers at y-yin.io/research and
published here because the filings are public domain and the parsing is fiddly
enough to be worth sharing. Read straight from each filing's infotable.xml —
the structured document EDGAR renders its own filing pages from — so no HTML is
scraped.
Coverage: 9 funds, 452… See the full description on the dataset page: https://huggingface.co/datasets/eagle0504/sec-13f-holdings.multireward-grpo-gsm8k-rewards-qwen2.5-7b
Multi-Reward GRPO — GSM8K Rewards (Qwen2.5-7B-Instruct)
Raw rollout-level reward observations from the empirical Section of
"Conditioned Multi-Reward Advantage Estimation: A Finite-Sample Analysis".
This is the data that produced the headline Theorem 3 (correlation-dependent
MSE floor) and Proposition 4 (sign-changing conditioning bias) figures on
real LLM rollouts. Each rollout was sampled from Qwen/Qwen2.5-7B-Instruct on
GSM8K test prompts at temperature 0.7.
What's in… See the full description on the dataset page: https://huggingface.co/datasets/eagle0504/multireward-grpo-gsm8k-rewards-qwen2.5-7b.eagle3-speculative-decoding-energy-sweep
EAGLE3 Speculative Decoding Energy Sweep
Per-config energy/throughput/latency measurements for EAGLE3 speculative decoding
(speculative_num_steps, speculative_eagle_topk, speculative_num_draft_tokens)
served with sglang, across batch sizes. Collected for an RL project that learns to
pick speculative-decoding parameters to hold GPU energy utilization in a target band.
Model: unsloth/Llama-3.2-1B-Instruct + rescommons/SpecForge-EAGLE3-Llama-3.2-1B-Instruct draft head.
Hardware:… See the full description on the dataset page: https://huggingface.co/datasets/Pradheep1647/eagle3-speculative-decoding-energy-sweep.cubestack-eaglecamThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "so_follower",
"total_episodes": 129,
"total_frames": 63332,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 30,
"splits": {
"train": "0:129"},
"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/Gueso/cubestack-eaglecam.multireward-grpo-fintech-customer-comms
Multi-Reward GRPO — Synthetic Fintech Customer Communications
Synthetic multi-turn customer-service conversations for a fictional bank
("Bank of XYZ"), generated for the empirical Section of "Conditioned
Multi-Reward Advantage Estimation: A Finite-Sample Analysis".
Each conversation ends with m parallel sampled bot replies, each scored
on three verifiable reward channels designed for fintech customer service.
This is the multi-reward GRPO group structure on a real generation… See the full description on the dataset page: https://huggingface.co/datasets/eagle0504/multireward-grpo-fintech-customer-comms.context-engineering-v1
Context Engineering V1: Sequential API Recommendation Dataset
This dataset accompanies the research paper:
Rethink Context Engineering Using an Attention-based Architecture
Yiqiao Yin — University of Chicago Booth School of Business / Columbia University
It was generated using the open-source context-engineer Python package:
GitHub: https://github.com/yiqiao-yin/context-engineer-repo
PyPI: https://pypi.org/project/context-engineer/0.1.0/
Dataset Summary
This dataset… See the full description on the dataset page: https://huggingface.co/datasets/eagle0504/context-engineering-v1.multireward-grpo-gsm8k-rewards
Multi-Reward GRPO — GSM8K Rewards (Qwen2.5-1.5B-Instruct)
Raw rollout-level reward observations from the empirical Section of
"Conditioned Multi-Reward Advantage Estimation: A Finite-Sample Analysis".
This is the data that produced the headline Theorem 3 (correlation-dependent
MSE floor) and Proposition 4 (sign-changing conditioning bias) figures on
real LLM rollouts.
What's in here
For each of 150 GSM8K test prompts, we sampled 16 independent seeds × 32
rollouts… See the full description on the dataset page: https://huggingface.co/datasets/eagle0504/multireward-grpo-gsm8k-rewards.gpt-4.1-nano-eagle-fs-and-filteredalpaca_Llama-3.1-8B-Instruct_eagle_kldivergence_baseGlobal_Eagle_Species_DatasetThis dataset is released under the MIT License. You are free to use, modify, merge, publish, distribute, sublicense, and/or sell copies of the data, provided that proper attribution is given. The data is intended for educational, research, and non-commercial or commercial use.
Team_Eagle_Rayalpaca_Llama-3.1-8B-Instruct_eagle_paraphrased_greedyalpaca_Llama-3.1-8B-Instruct_eagle_divergence_basealpaca_Llama-3.1-8B-Instruct_eagle_greedy_divergence