PalladiumData/Palladium-1M-Preview

💎 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).

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📋 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

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📊 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

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🔬 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:

1. Entropy Analysis: We measure the compressibility of every document using ZSTD compression ratios. Low entropy (highly compressible) text indicates repetition, boilerplate, or SEO spam.
2. Sophistication Scoring: We map the linguistic complexity using grade-level heuristics and vocabulary density.
3. 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.

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🛠️ 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])

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🔐 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