README.md

---
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

![Quality Dashboard](quality_dashboard.png)

![Token Distribution](token_distribution.png)

![Domain Distribution](domain_distribution.png)

![Edu Score Distribution](edu_score_distribution.png)

![Compression Ratios](compression_distribution.png)

![Grade Levels](grade_level_distribution.png)

---

## 📊 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).

<p align="center">
  <img src="palladium_demo_victory.jpg" width="70%" alt="Palladium Victory Graph">
</p>

| 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:

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.

---

## 🛠️ Usage

This dataset is compatible with the Hugging Face `datasets` library.

```python
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](mailto:scott@palladiumtrain.com)
* **Web:** [palladiumtrain.com](https://www.palladiumtrain.com)
* **Organization:** Palladium Data