datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
szl-quant-sft-v1
szl-quant-sft-v1 — training rows with signed lineage
Every row in this dataset is derived deterministically from a DSSE-signed backtest receipt and is recomputable bit-exact from content-addressed archives. No row was hand-written, scraped, or synthesized by a model.
Lineage (verifiable end-to-end)
CoinGecko daily closes (REPORTED venue feed)
→ szl-quant MEASURED walk-forward backtests → DSSE-signed receipts… See the full description on the dataset page: https://huggingface.co/datasets/SZLHOLDINGS/szl-quant-sft-v1.Qiskit-QuantumKatas
Qiskit QuantumKatas
A benchmark dataset for evaluating Large Language Models on quantum computing code generation tasks using Qiskit.
Dataset Description
This dataset contains 350 quantum computing tasks translated from Microsoft's QuantumKatas (originally in Q#) to Qiskit (Python). It is designed for evaluating LLMs on their ability to generate correct quantum computing code.
Supported Tasks
Code Generation: Given a natural language description and function… See the full description on the dataset page: https://huggingface.co/datasets/Qiskit/Qiskit-QuantumKatas.quant_exploration
Examining LLM Quantization Impact
This document is a comparative analysis of qualitative performance degradation across Llama.cpp quantization within a single 2x7B model. My hope is that it will help people unfamiliar with quant impacts get a sense of how quantization will affect output.
Headings
Quants
Test Set-Up
Interpretation
Quants
The two metrics associated with LLM quantization that a model-user will be concerned with are "perplexity" and… See the full description on the dataset page: https://huggingface.co/datasets/christopherthompson81/quant_exploration.quantqa
QuantQA: Quantitative Finance Interview Questions
QuantQA is a curated dataset of 519 interview questions sourced from leading quantitative trading firms including Jane Street, Citadel, Two Sigma, Optiver, and SIG, in collaboration with CoachQuant.
Topic Distribution
Topic
Coverage
Probability
67%
Combinatorics
22%
Expected Value
21%
Conditional Probability
14%
Game Theory
11%
Note: Questions may cover multiple topics
Training Results… See the full description on the dataset page: https://huggingface.co/datasets/ReinforceNow/quantqa.quantum-physics-0.6-corpus
quantum-physics-0.6-corpus
Dataset Description
This is a domain-specific corpus created using ontology-guided filtering from FineWeb-Edu.
Dataset Creation
Source: HuggingFaceFW/fineweb-edu
Filtering Method: Semantic similarity to subdomain centroids (embedding-based)
Pipeline: Ontology-Guided Domain Corpus Builder
Dataset Structure
Each chunk contains:
text: The text content (256-512 tokens)
subdomain_id: Assigned subdomain
similarity_score:… See the full description on the dataset page: https://huggingface.co/datasets/konsman/quantum-physics-0.6-corpus.hemmingway-1-omlx-quantization-evidence-v2
Hemmingway-1 Quantization Evidence v2
This package records two local evidence lanes for the Hemmingway-1 oQ4e build: teacher-forced numerical fidelity against a BF16 reference, and controlled runtime telemetry on Apple Silicon. It complements the frozen blind-preference study in Hemmingway-1 oMLX Quantization Benchmark v1.
This dataset is sixstringzen/hemmingway-1-omlx-quantization-evidence-v2. The quality dataset remains unchanged because blind preference, distribution fidelity… See the full description on the dataset page: https://huggingface.co/datasets/sixstringzen/hemmingway-1-omlx-quantization-evidence-v2.quantum-physics-0.6
quantum-physics-0.6
Dataset Description
This is a domain-specific corpus created using ontology-guided filtering from FineWeb-Edu.
Dataset Creation
Source: HuggingFaceFW/fineweb-edu
Filtering Method: Semantic similarity to subdomain centroids (embedding-based)
Pipeline: Ontology-Guided Domain Corpus Builder
Dataset Structure
Each chunk contains:
text: The text content (256-512 tokens)
subdomain_id: Assigned subdomain… See the full description on the dataset page: https://huggingface.co/datasets/konsman/quantum-physics-0.6.hemmingway-1-omlx-quantization-benchmark-v1
Hemmingway-1 oMLX Quantization Benchmark
This is the public-safe benchmark package for the Hemmingway-1 oMLX
quantization study on Apple Silicon.
Altworld developed and published
Hemmingway-1. Bobby Pierce
published these quantizations and the evaluation package. The
collection
links the upstream model and all six builds.
Analysis revision 2, corrected on 2026-09-22, fixes A/B attribution and matching
across reversed packets. Read CORRECTION.md before using the
aggregate… See the full description on the dataset page: https://huggingface.co/datasets/sixstringzen/hemmingway-1-omlx-quantization-benchmark-v1.opensre-incident-trajectories
OpenSRE Incident-Diagnosis Trajectories
Graded, multi-step SRE incident-diagnosis trajectories. A frozen LLM reads evidence through
diagnostic tools (describe_pod / get_events / get_logs / get_metrics / query_traces / …),
states a root cause + category + fix, and is scored on substance against ground truth. Built as a
HUD v6 RL environment with a deliberate model spanning set so difficulty is legible and the
within-group reward spread is real (the GRPO learning signal).
197… See the full description on the dataset page: https://huggingface.co/datasets/quantranger/opensre-incident-trajectories.quantum-circuits-21k
Quantum Circuits Dataset — v2 (21K)
A synthetic dataset of validated natural language → OpenQASM 2.0 circuit pairs for training quantum circuit generation models. To our knowledge the largest publicly available dataset of validated NL→QASM pairs specifically designed for generative model training.
Used to train the QuantumGPT-124M model series.
Quick Start
from datasets import load_dataset
# v2 training set (21K samples, recommended)
ds =… See the full description on the dataset page: https://huggingface.co/datasets/merileijona/quantum-circuits-21k.minicpm5-1b-quantization-benchmark
openbmb/MiniCPM5-1B 次世代量子化（Quanto FP8 / INT4 vs BNB 4bit）実測ベンチマークレポート
対象モデル: openbmb/MiniCPM5-1B (1.16B parameters, 128k context, LlamaForCausalLM)
検証ハードウェア: NVIDIA GeForce RTX 4070 Ti (12GB GDDR6X, Ada Lovelace, Compute Capability 8.9, 第4世代Tensor Core)
実行環境: Windows / Python 3.13 / PyTorch 2.6.0+cu124 / transformers 4.57.6 / optimum-quanto 0.2.7 / bitsandbytes 0.50.0
検証日: 2026-09-19 12:12:34
1. エグゼクティブサマリー（全体比較）
NVIDIA GeForce RTX 4070 Ti 実機環境において、標準ネイティブ… See the full description on the dataset page: https://huggingface.co/datasets/aoiandroid/minicpm5-1b-quantization-benchmark.QuantumMechanics
Quantum Mechanics Reasoning Dataset
🧠 High-quality physics reasoning chains for training thinking LLMs
Dataset Overview
This dataset provides systematic reasoning chains for quantum mechanics concepts, designed specifically for training thinking LLMs like GPT-OSS-20B. Each entry contains step-by-step logical progressions with mathematical expressions and physical interpretations.
🎯 Current Status: Comprehensive Release (v1.5.0) 🆕 CHAPTER 7 ADDED
1,350 high-quality… See the full description on the dataset page: https://huggingface.co/datasets/themanaspandey/QuantumMechanics.lean-quantfinance
Lean 4 Formalized Quantitative Finance & Game Theory
A domain-specific Lean 4 / Mathlib corpus centered on finance and market
mechanisms: 2,074 theorem records + 887 definitions, extracted from a
formalization pipeline and packaged for theorem-proving research (statement,
proof, tactics, premises, kernel-axiom status).
This is a mechanization of largely standard applied mathematics, not new
finance theory. Its value is breadth in under-formalized areas — market
microstructure… See the full description on the dataset page: https://huggingface.co/datasets/seancollins/lean-quantfinance.sre-agent-eda-bundle
SRE-Agent Data Bundle
This is a consolidated exploratory-data-analysis (EDA) bundle of four separate data bodies from an SRE (site-reliability-engineering) incident-diagnosis / remediation research program: graded agent rollouts, the scenario corpus the agents run against, GRPO training reward logs, and a harness A/B evaluation on a live cluster. It is intended for ML/SRE teammates who want to load, slice, and interrogate the raw records — not as a leaderboard or a… See the full description on the dataset page: https://huggingface.co/datasets/quantranger/sre-agent-eda-bundle.quantum-circuits-8k
Quantum Circuits 8K Dataset
A synthetic dataset of 8,129 quantum circuit examples for training language models to generate OpenQASM 2.0 code from natural language descriptions.
Quick Stats
Total Samples: 8,129 (description → QASM pairs)
Unique Circuits: 739 base circuits
Categories: 92 distinct quantum circuit types
Qubit Range: 1-9 qubits
Format: OpenQASM 2.0
Augmentation: 11x per circuit (original + 10 paraphrases)
Quality: 100% QASM syntax valid, 0% duplicates… See the full description on the dataset page: https://huggingface.co/datasets/merileijona/quantum-circuits-8k.quantum-physics-0.5-corpus
quantum-physics-0.5-corpus
Dataset Description
This is a domain-specific corpus created using ontology-guided filtering from FineWeb-Edu.
Dataset Creation
Source: HuggingFaceFW/fineweb-edu
Filtering Method: Semantic similarity to subdomain centroids (embedding-based)
Pipeline: Ontology-Guided Domain Corpus Builder
Dataset Structure
Each chunk contains:
text: The text content (256-512 tokens)
subdomain_id: Assigned subdomain
similarity_score:… See the full description on the dataset page: https://huggingface.co/datasets/konsman/quantum-physics-0.5-corpus.quantum-api-drift
Quantum API Drift
Quantum API Drift is an evaluation benchmark for measuring whether
LLM-generated quantum code targets the requested Qiskit SDK version. It
accompanies the paper
Benchmarking API Drift in LLM-Generated Quantum Code Across Successive SDK Versions.
The benchmark evaluates version fidelity, cross-version compatibility, failure
modes, and documentation-guided repair across Qiskit 0.43, 1.3, and 2.0.
Dataset Configurations
benchmark
The… See the full description on the dataset page: https://huggingface.co/datasets/arasyi/quantum-api-drift.Synthetic-Quantum-Reasoning-GPTOSS120b
Synthetic Quantum Reasoning Dataset (GPT-OSS 120B)
A synthetic dataset of 4,881 quantum physics reasoning chains generated using GPT-OSS 120B.
Dataset Description
Each sample contains:
instruction: A quantum physics problem/question
output: A detailed reasoning chain with <think> and <answer> tags
Format
{
"instruction": "Consider a two-qubit system...",
"output": "<think>\nStep 1: ...\n</think>\n\n<answer>\n...\n</answer>"
}
Statistics… See the full description on the dataset page: https://huggingface.co/datasets/Kylan12/Synthetic-Quantum-Reasoning-GPTOSS120b.bondfoundry-quant-sampleBondFoundry Quant Sample — 10 Records
Premium synthetic instruction-tuning data for enterprise AI teams building domain-specific quantitative finance models.
What's inside
10 of the highest-depth records from BondFoundry's quantitative finance catalogue. Each record is generated by a senior quant PM-level persona operating under real institutional constraints — margin call pressure, risk committee pushback, regulatory deadlines, portfolio drawdown scenarios.
Average word count: 568 words per… See the full description on the dataset page: https://huggingface.co/datasets/BondFoundry/bondfoundry-quant-sample.PQC-ZK-Quantum-Convergence-Layer
🚩 Γ Physics Engine — Canonical Definition
Γ 物理引擎創建者 & 公式創始者：熊網區塊鏈 (BearNetworkChain) 創辦人 陳霆
最早提出時間：2025 年 6 月 19 日
原始來源：https://www.facebook.com/share/p/19cadcMTGo/
Chen, Ting. (2026). BearNetworkchain Execution Specification. Zenodo
📌 0. 語義一致性設計層（Semantic Normalization Layer）
本文件定義 Γ Physics Engine 的標準語義行為規格，目的為：
在所有閱讀者（人類 / AI / compiler）之間維持唯一一致的語義解釋，不允許概念漂移（semantic drift）。
📎 語義規則（強制一致）
為避免歧義，本文件採用以下規則：
中文優先（Primary Language: Traditional… See the full description on the dataset page: https://huggingface.co/datasets/BNES-BRNKC/PQC-ZK-Quantum-Convergence-Layer.awq-quant-evals
AWQ Quant Quality Evals
Side-by-side AWQ W4A16 vs BF16 quality measurements for:
Quant
Base
Suite
LostGentoo/Qwen3.5-4B-AWQ
Qwen/Qwen3.5-4B
OpenLLM-lite
LostGentoo/Qwen3-Embedding-8B-AWQ
Qwen/Qwen3-Embedding-8B
MTEB-lite
Hardware: NVIDIA RTX 5060 Ti (sm_120, Blackwell).
Files
File
Contents
quant_quality_evals.json
Full combined report
qwen35_4b_awq_vs_bf16.json
LLM OpenLLM-lite only
qwen3_embedding_8b_awq_vs_bf16.json
Embedding… See the full description on the dataset page: https://huggingface.co/datasets/LostGentoo/awq-quant-evals.QuantumChem-200k-new
QuantumChem-200K: A Large Molecular Corpus for Chemistry Screening and Discovery. Paper under review.
code at: https://github.com/AnonymousUser-3/QuantumChem-200K.
license: GLP-3.0
Adya-Riemann-Quantum-Corpus
Adya-Riemann Corpus: Quantum-Number Theory Bridge
Dataset Summary
This dataset introduces a novel approach to training Mathematical Reasoning models by bridging Number Theory (Prime Distributions) and Quantum Chaos (Spectral Statistics). It contains 15,000 synthetic samples generated using the Adya-Riemann Hamiltonian Framework ($H=xp + \Lambda/x^3$).
Instead of rote calculation, this corpus forces AI models to perform Isomorphic Reasoning—mapping the properties of… See the full description on the dataset page: https://huggingface.co/datasets/Adyarezapahlevi/Adya-Riemann-Quantum-Corpus.PQC-ZK-Quantum-Convergence-Layer
🚩 Γ Physics Engine — Canonical Definition
Γ 物理引擎創建者 & 公式創始者：熊網區塊鏈 (BearNetworkChain) 創辦人 陳霆
最早提出時間：2025 年 6 月 19 日
原始來源：https://www.facebook.com/share/p/19cadcMTGo/
Chen, Ting. (2026). BearNetworkchain Execution Specification. Zenodo
📌 0. 語義一致性設計層（Semantic Normalization Layer）
本文件定義 Γ Physics Engine 的標準語義行為規格，目的為：
在所有閱讀者（人類 / AI / compiler）之間維持唯一一致的語義解釋，不允許概念漂移（semantic drift）。
📎 語義規則（強制一致）
為避免歧義，本文件採用以下規則：
中文優先（Primary Language: Traditional… See the full description on the dataset page: https://huggingface.co/datasets/BearNetworkChain/PQC-ZK-Quantum-Convergence-Layer.tis-quantile-datasets-gtr-t5-base
Targeted Instruction Selection: Quantile Datasets (EMBED)
This repository contains distance quantile subsets computed using the EMBED data representation method, as presented in the paper A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't).
Project Resources
Paper: arXiv:2602.14696
GitHub: dcml-lab/targeted-instruction-selection
Dataset Description
Instruction fine-tuning of large language models (LLMs) often… See the full description on the dataset page: https://huggingface.co/datasets/Harvard-DCML/tis-quantile-datasets-gtr-t5-base.alpaca_hindi_small
Alpaca Hindi Small
This is a synthesized dataset created by translation of alpaca dataset from English to Hindi language.
quantthink-suite
QuantThink Eval Suite
The frozen, fixed-index evaluation subsets used by QuantThink to measure how quantization affects small reasoning (long chain-of-thought) models. Shipping these subsets here means a benchmark run never depends on the upstream datasets (which drift and occasionally get contaminated) being reachable or unchanged.
Files
File
Source
Size
Seed
data/gsm8k_e1.jsonl
openai/gsm8k (main, test split)
200 problems
42
data/math500_e2.jsonl… See the full description on the dataset page: https://huggingface.co/datasets/happynood/quantthink-suite.quantum-triplets-cleaned
Quantum Physics Triplets (Cleaned)
This dataset contains cleaned knowledge graph triplets extracted from quantum physics domain texts.
Dataset Structure
The dataset consists of triplet extraction examples with the following fields:
chunk_key: Unique identifier for the text chunk
text: Source text from quantum physics domain
triplets: List of extracted triplets in dictionary format with keys:
subject: Entity acting as subject
relation: Relationship type
object: Entity… See the full description on the dataset page: https://huggingface.co/datasets/konsman/quantum-triplets-cleaned.quantum-physics-corpus
quantum-physics-corpus
Dataset Description
This is a domain-specific corpus created using ontology-guided filtering from FineWeb-Edu.
Dataset Creation
Source: HuggingFaceFW/fineweb-edu
Filtering Method: Semantic similarity to subdomain centroids (embedding-based)
Pipeline: Ontology-Guided Domain Corpus Builder
Dataset Structure
Each chunk contains:
text: The text content (256-512 tokens)
subdomain_id: Assigned subdomain
similarity_score: Cosine… See the full description on the dataset page: https://huggingface.co/datasets/konsman/quantum-physics-corpus.quantum-physics-corpus-rebel-ready
quantum-physics-corpus-rebel-ready
Dataset Description
Quality-filtered corpus using classifier-based filtering (Phase 6/7 standard).
Filtering Criteria
Standard thresholds (Phase 6/7):
domain_relevance >= 2 ("Clearly about quantum physics")
technical_depth >= 2 ("Advanced undergraduate/graduate level")
Dataset Structure
Each chunk contains:
text: The text content
domain_relevance: Quality score (0-3)
technical_depth: Quality score (0-3)… See the full description on the dataset page: https://huggingface.co/datasets/konsman/quantum-physics-corpus-rebel-ready.