Datasets:
license: cc-by-nc-4.0
task_categories:
- text-generation
language:
- en
size_categories:
- 1M<n<10M
tags:
- physics-filtering
- information-theory
- entropy-maximization
- clean-data
- data-curation
- pretraining
pretty_name: Palladium-1M
configs:
- config_name: default
data_files:
- split: train
path: palladium_sample_10k.jsonl
💎 Palladium-1M: High-Density Information for Efficient LLM Training
Palladium-1M is a curated dataset of ~1 million high-entropy, high-sophistication documents (13.5GB), mined from the open web using a novel Physics-Based Filtration System.
Unlike standard filters that rely on heuristics or keywords, the Palladium Refinery uses Information Theory (ZSTD Compression Ratios) and Linguistic Density to mathematically distinguish "Signal" from "Noise."
The result is a dataset that trains models significantly faster and achieves lower perplexity per compute unit compared to standard web corpora (e.g., FineWeb).
📋 Datasheet
| Metric | Value |
|---|---|
| Documents (preview) | 10,000 |
| Documents (full dataset) | ~1,000,000 |
| Full Dataset Size | 13.5 GB |
| Total Tokens (preview) | 23,665,387 (23.7M) |
| Tokens/Doc (mean) | 2,367 |
| Tokens/Doc (median) | 1,296 |
| Tokens/Doc (range) | 112 – 102,832 |
| Compression Ratio (mean) | 2.32x |
| Reading Level (mean) | Grade 11.1 |
| Edu Score (mean) | 3.76 |
| Edu Score (median) | 3.72 |
| Tokenizer | cl100k_base (BPE) |
Domain Distribution
| Domain | Docs | % |
|---|---|---|
| Biology / Medicine | 3,321 | 33.2% |
| Computer Science | 1,354 | 13.5% |
| Earth / Environmental Science | 1,245 | 12.4% |
| General / Other | 982 | 9.8% |
| Mathematics | 901 | 9.0% |
| Physics | 656 | 6.6% |
| Engineering | 588 | 5.9% |
| Law / Policy | 379 | 3.8% |
| Chemistry | 325 | 3.2% |
| Economics / Finance | 181 | 1.8% |
| Philosophy / Humanities | 68 | 0.7% |
Data Quality Visualizations
📊 The "Palladium Advantage" (Benchmark Results)
To verify the quality of the data, we conducted a controlled "Battle Run" fine-tuning a Qwen 2.5 (1.5B) model.
- Control Group: Standard "FineWeb" (Dirty Web Data).
- Experimental Group: Palladium-1M (Physics-Filtered Data).
- Training Duration: 1 Epoch Equivalent (30 Steps).
Key Result: 12.5% Lower Loss
The model trained on Palladium-1M achieved a 12.5% lower final loss than the control group, with significantly higher training stability (lower gradient norm variance).
| Metric | Dirty Web (FineWeb) | Palladium-1M (Clean) | Improvement |
|---|---|---|---|
| Final Loss | 2.58 | 2.26 | -12.5% |
| Gradient Stability | High Variance | Smooth Convergence | Significant |
🔬 Methodology: The Physics of Information
Most datasets are filtered by "Quality Classifiers" (LLMs trained to spot bad text). This is circular and expensive.
Project Palladium takes a first-principles approach:
- Entropy Analysis: We measure the compressibility of every document using ZSTD compression ratios. Low entropy (highly compressible) text indicates repetition, boilerplate, or SEO spam.
- Sophistication Scoring: We map the linguistic complexity using grade-level heuristics and vocabulary density.
- The "Goldilocks" Zone: We discard the bottom ~90% of the web that falls below our Signal-to-Noise Threshold.
The remaining ~10% is Palladium: Pure, dense information.
🛠️ Usage
This dataset is compatible with the Hugging Face datasets library.
from datasets import load_dataset
# Load the Preview (10K Samples)
dataset = load_dataset("PalladiumData/Palladium-1M-Preview", split="train")
print(f"Documents: {len(dataset)}")
print(dataset[0])
🔐 Access & Licensing
This repository contains a 10,000-document preview of the full dataset.
The full 13.5GB Industrial Dataset (1M+ Docs) is available for commercial licensing. It is designed for:
- Pre-training small language models (1B–7B) that need to be data-efficient.
- Fine-tuning specialized models for finance, law, science, or engineering.
- RAG systems that need high-quality knowledge bases without boilerplate.
For full access, commercial licensing, or custom Refinery curation services:
- Email: scott@palladiumtrain.com
- Web: palladiumtrain.com
- Organization: Palladium Data