ALEA Preprocess | mike bommarito

An efficient, accessible data preprocessing library designed to handle the demanding requirements of large language model training pipelines, including support for KL3M and other pretraining datasets.

Overview

ALEA Preprocess addresses the critical need for high-performance data preprocessing in LLM development. By leveraging compiled Rust code with Python bindings, it provides the speed necessary for processing trillion-token datasets while maintaining ease of use.

Core Capabilities

Preprocessing Modes

Pretraining: Large-scale text corpus preparation
Supervised Fine-Tuning (SFT): Instruction dataset formatting
Direct Preference Optimization (DPO): Preference pair preparation
Custom Pipelines: Extensible architecture for new formats

Performance Features

Rust Backend: Compiled performance for heavy processing
Python Interface: Familiar API for ML practitioners
Streaming Processing: Memory-efficient for large datasets
Parallel Execution: Multi-core utilization

Technical Architecture

Language Stack

Core Engine: Rust for performance-critical operations
Python Bindings: maturin for seamless integration
API Design: Pythonic interfaces hiding complexity

Processing Pipeline

Input Handling: Multiple format support
Tokenization: Integration with various tokenizers
Filtering: Quality and safety checks
Transformation: Format conversions
Output Generation: Optimized data structures

Installation

From PyPI

pip install alea-preprocess

Development Setup

# Clone repository
git clone https://github.com/alea-institute/alea-preprocess

# Install with Poetry and maturin
poetry install
poetry run maturin develop

Key Features

Data Quality

Deduplication algorithms
Language detection
Content filtering
Format validation

Efficiency Optimizations

Zero-copy operations where possible
Lazy evaluation strategies
Batched processing
Cache-friendly algorithms

Format Support

Raw text documents
JSON/JSONL formats
Parquet files
Custom format plugins

Integration with KL3M

Specifically designed to support:

KL3M data pipeline requirements
Copyright-clean data processing
Legal document handling
Domain-specific preprocessing

Use Cases

Pretraining Preparation

Web crawl processing
Document deduplication
Quality filtering
Format standardization

Fine-Tuning Datasets

Instruction formatting
Response pairing
Quality scoring
Balance checking

Preference Learning

Preference pair creation
Ranking data preparation
Reward model training data
Human feedback processing

Example Usage

from alea_preprocess import PreprocessPipeline

# Initialize pipeline
pipeline = PreprocessPipeline(
    mode="pretrain",
    tokenizer="kl3m-004-128k-cased",
    quality_threshold=0.8
)

# Process dataset
processed = pipeline.process_dataset(
    input_path="raw_data/",
    output_path="processed/",
    num_workers=8
)

Development Status

Currently in active development:

Core functionality implemented
Performance optimizations ongoing
Additional format support planned
Documentation expansion in progress

Testing

Comprehensive test suite available:

Unit tests for core functions
Integration tests with real data
Performance benchmarks
Example notebooks in tests/

Community

Open-source development approach:

Issue tracking on GitHub
Contribution guidelines
Regular updates
Community feedback integration

Future Roadmap

Planned enhancements:

Additional preprocessing modes
GPU acceleration options
Distributed processing support
Enhanced filtering algorithms

ALEA Preprocess represents essential infrastructure for modern LLM development, providing the tools needed to efficiently transform raw data into training-ready datasets while maintaining quality and compliance standards.