Training Pipeline¶

This document describes the training pipeline used in the Bluefly LLM ecosystem.

Overview¶

The Bluefly training pipeline is a comprehensive system for training, fine-tuning, and evaluating machine learning models. The pipeline is designed to be flexible, scalable, and reproducible, enabling consistent model development across the ecosystem.

Pipeline Architecture¶

The training pipeline consists of several components:

Data Collection: Gathering and storing training data
Data Processing: Cleaning, transforming, and augmenting data
Model Training: Training and fine-tuning models
Model Evaluation: Assessing model performance
Model Registry: Storing and versioning models
Deployment: Deploying models to production

Data Collection¶

The data collection component is responsible for gathering training data from various sources.

Data Sources¶

User Interactions: Logs of user interactions with the system
Feedback Data: Explicit feedback provided by users
External Datasets: Public or licensed datasets for specific tasks
Synthetic Data: Generated data for augmentation or testing

Data Storage¶

Collected data is stored in a structured format:

Raw Data: Original, unprocessed data
Metadata: Information about the data (source, timestamp, etc.)
Annotations: Labels or annotations added to the data

Data Processing¶

The data processing component prepares the data for training.

Processing Steps¶

Cleaning: Removing noise, errors, and outliers
Normalization: Standardizing data formats
Tokenization: Converting text to tokens
Augmentation: Generating additional training examples
Filtering: Removing sensitive or low-quality data

Data Validation¶

The pipeline includes validation steps to ensure data quality:

Schema Validation: Ensuring data adheres to expected formats
Quality Checks: Detecting and handling anomalies
Distribution Analysis: Verifying data distributions match expectations

Model Training¶

The model training component handles the actual training and fine-tuning of models.

Training Methods¶

Pre-training: Training models from scratch on large datasets
Fine-tuning: Adapting pre-trained models to specific tasks
Parameter-Efficient Fine-tuning: Using techniques like LoRA for efficient adaptation
Reinforcement Learning from Human Feedback (RLHF): Training models based on human preferences

Training Infrastructure¶

The pipeline utilizes scalable infrastructure for training:

Distributed Training: Training across multiple machines
GPU Acceleration: Utilizing GPUs for faster training
Checkpointing: Saving intermediate model states
Experiment Tracking: Logging metrics and parameters during training

Model Evaluation¶

The model evaluation component assesses model performance.

Evaluation Metrics¶

Task-Specific Metrics: Accuracy, F1 score, BLEU, ROUGE, etc.
General Metrics: Perplexity, loss
Behavioral Metrics: Safety, helpfulness, honesty
Efficiency Metrics: Inference speed, memory usage

Evaluation Datasets¶

Models are evaluated on diverse datasets:

Held-out Test Sets: Data not seen during training
Benchmark Datasets: Standard datasets for comparing models
Adversarial Datasets: Datasets designed to test model robustness
Real-world Scenarios: Tests mimicking actual use cases

Model Registry¶

The model registry manages model versions and artifacts. See the Model Registry document for details.

Deployment¶

The deployment component moves models to production.

Deployment Process¶

Model Packaging: Preparing models for deployment
Infrastructure Provisioning: Setting up servers or containers
Deployment: Moving models to production infrastructure
Monitoring: Setting up metrics and alerts

Deployment Patterns¶

Blue-Green Deployment: Maintaining two production environments
Canary Deployment: Gradually shifting traffic to new models
Shadow Deployment: Testing new models in parallel with existing ones

Best Practices¶

Data Management¶

Version Control: Keep track of dataset versions
Documentation: Document dataset properties and preprocessing steps
Privacy: Remove personal information and sensitive data

Training Management¶

Reproducibility: Use fixed random seeds and version control for configuration
Resource Efficiency: Optimize batch sizes and precision formats
Checkpointing: Save intermediate models to resume training

Evaluation Management¶

Comprehensive Testing: Evaluate models on diverse metrics and datasets
Regression Testing: Ensure new models don't regress on key capabilities
Bias and Fairness: Evaluate models for biases and fairness issues