Fine Tuning Strategy¶

Strategy outline and development plan for fine-tuning foundation models on Thich Nhat Hanh translations.

Initial Prompt¶

hello claude,

I'd like you to tell me how you would go about fine-tuning an openai gpt-4o model or an anthropic model to improve translations of thich nhat hanh's works between the languages he taught in: Vietnamese, English, French. This is a project initiated by the plum village community with full access to collected works and permission to develop this for internal use only (for translation purposes). It would be of secondary benefit if the model also learned something about thay's teachings and could reflect, answer questions and so on.

give a high level design strategy and then a detailed plan, with initial skeleton development in python using stubs for functions and classes. be as thorough and complete as possible. Consider an arc of development from prototyping and testing phase through initial development to a final stage of full development.

Outputs are

1) design plan and overview with considerations. 2) process sequence from prototyping and testing through full implementation 3) initial skeleton structures: including file structure blueprint with all major modules, and all major classes and high level functions as stubs. For minor modules these can simply be listed in the file structure blueprint.

Translation Project Structure¶

# Project Structure

tnh_translation/
│
├── pyproject.toml
├── setup.py
├── README.md
├── requirements.txt
│
├── config/
│   ├── model_config.yaml
│   ├── training_config.yaml
│   └── evaluation_config.yaml
│
├── notebooks/
│   ├── data_exploration.ipynb
│   ├── model_experimentation.ipynb
│   └── evaluation_analysis.ipynb
│
├── src/
│   ├── data/
│   │   ├── __init__.py
│   │   ├── corpus.py
│   │   ├── preprocessing.py
│   │   ├── alignment.py
│   │   └── validation.py
│   │
│   ├── models/
│   │   ├── __init__.py
│   │   ├── base_model.py
│   │   ├── fine_tuning.py
│   │   └── serving.py
│   │
│   ├── training/
│   │   ├── __init__.py
│   │   ├── trainer.py
│   │   ├── callbacks.py
│   │   └── optimization.py
│   │
│   ├── evaluation/
│   │   ├── __init__.py
│   │   ├── metrics.py
│   │   ├── analysis.py
│   │   └── visualization.py
│   │
│   └── utils/
│       ├── __init__.py
│       ├── logging.py
│       ├── config.py
│       └── helpers.py
│
├── tests/
│   ├── test_data/
│   ├── test_models/
│   ├── test_training/
│   └── test_evaluation/
│
└── scripts/
    ├── prepare_data.py
    ├── train_model.py
    └── evaluate_model.py

Core Implementation Classes¶

# src/data/corpus.py

from pathlib import Path
from typing import Dict, List, Optional, Union
from dataclasses import dataclass
import logging

logger = logging.getLogger(__name__)

@dataclass
class TextUnit:
    """Represents a single unit of text (paragraph, sentence, etc.) in multiple languages"""
    id: str
    content: Dict[str, str]  # Language code -> text
    metadata: Dict[str, any]
    source_file: Path
    alignment_score: Optional[float] = None

class ParallelCorpus:
    """Manages multilingual parallel text corpus for training"""

    def __init__(self, base_path: Path):
        self.base_path = Path(base_path)
        self.texts: List[TextUnit] = []
        self.language_pairs: List[tuple] = []

    def load_texts(self, source_paths: Dict[str, Path]):
        """Load texts from multiple sources with language mapping"""
        pass

    def align_texts(self, method: str = "sentence"):
        """Align texts across languages using specified method"""
        pass

    def validate_alignment(self, threshold: float = 0.8):
        """Validate alignment quality"""
        pass

    def export_training_data(self, output_path: Path):
        """Export aligned texts in training format"""
        pass

# src/data/preprocessing.py

class TextPreprocessor:
    """Handles text preprocessing for training data"""

    def __init__(self, config: Dict):
        self.config = config

    def clean_text(self, text: str) -> str:
        """Clean and normalize text"""
        pass

    def tokenize(self, text: str) -> List[str]:
        """Tokenize text using appropriate method"""
        pass

    def handle_special_terms(self, text: str) -> str:
        """Process domain-specific terminology"""
        pass

# src/models/base_model.py

from abc import ABC, abstractmethod
from typing import Optional, Dict, Any

class BaseModel(ABC):
    """Abstract base class for model implementations"""

    def __init__(self, model_name: str, config: Dict[str, Any]):
        self.model_name = model_name
        self.config = config

    @abstractmethod
    def load_model(self):
        """Load pre-trained model"""
        pass

    @abstractmethod
    def prepare_inputs(self, texts: List[str]) -> Dict:
        """Prepare inputs for model"""
        pass

    @abstractmethod
    def train_step(self, batch: Dict) -> Dict:
        """Perform single training step"""
        pass

class GPT4Model(BaseModel):
    """GPT-4 model implementation"""

    def __init__(self, config: Dict[str, Any]):
        super().__init__("gpt-4", config)

    def load_model(self):
        """Load GPT-4 model"""
        pass

    def prepare_inputs(self, texts: List[str]) -> Dict:
        """Prepare inputs for GPT-4"""
        pass

# src/training/trainer.py

class ModelTrainer:
    """Handles model training and fine-tuning process"""

    def __init__(
        self,
        model: BaseModel,
        train_data: ParallelCorpus,
        val_data: ParallelCorpus,
        config: Dict[str, Any]
    ):
        self.model = model
        self.train_data = train_data
        self.val_data = val_data
        self.config = config

    def train(self):
        """Execute training loop"""
        pass

    def validate(self):
        """Run validation"""
        pass

    def save_checkpoint(self):
        """Save model checkpoint"""
        pass

# src/evaluation/metrics.py

class TranslationEvaluator:
    """Evaluates translation quality using multiple metrics"""

    def __init__(self, metrics: List[str]):
        self.metrics = metrics

    def evaluate(
        self,
        predictions: List[str],
        references: List[str]
    ) -> Dict[str, float]:
        """Calculate evaluation metrics"""
        pass

    def analyze_errors(
        self,
        predictions: List[str],
        references: List[str]
    ) -> Dict[str, Any]:
        """Perform error analysis"""
        pass

# src/evaluation/analysis.py

class QualityAnalyzer:
    """Analyzes translation quality and provides detailed feedback"""

    def __init__(self, config: Dict[str, Any]):
        self.config = config

    def analyze_terminology(self, text: str) -> Dict[str, Any]:
        """Analyze terminology usage"""
        pass

    def check_doctrinal_accuracy(self, text: str) -> Dict[str, Any]:
        """Verify doctrinal accuracy"""
        pass

    def generate_report(self, analysis_results: Dict[str, Any]) -> str:
        """Generate detailed analysis report"""
        pass

# Configuration and setup

def setup_logging():
    """Configure logging for the project"""
    pass

def load_config(config_path: Path) -> Dict[str, Any]:
    """Load configuration from YAML file"""
    pass

def initialize_project(config: Dict[str, Any]):
    """Initialize project with configuration"""
    pass

Training Configuration¶

# Model Configuration
model:
  name: "gpt-4"
  version: "latest"
  max_sequence_length: 2048
  batch_size: 16
  learning_rate: 2e-5
  warmup_steps: 1000
  max_steps: 50000
  save_steps: 1000
  eval_steps: 500

# Data Configuration
data:
  languages:
    - "vi"
    - "en"
    - "fr"
  text_units:
    - "sentence"
    - "paragraph"
  alignment:
    method: "hybrid"  # sentence + semantic
    min_score: 0.8
  preprocessing:
    lowercase: false
    normalize_unicode: true
    special_tokens:
      - "<<dharma>>"
      - "<<sangha>>"
      - "<<buddha>>"

# Training Configuration
training:
  objectives:
    - name: "translation"
      weight: 1.0
    - name: "understanding"
      weight: 0.5
  optimization:
    optimizer: "adamw"
    weight_decay: 0.01
    gradient_clip: 1.0
  scheduling:
    type: "linear"
    num_warmup_steps: 1000
  validation:
    frequency: 1000
    metrics:
      - "bleu"
      - "ter"
      - "comet"
      - "dharma_accuracy"

# Evaluation Configuration
evaluation:
  metrics:
    - name: "bleu"
      weight: 0.3
    - name: "ter"
      weight: 0.2
    - name: "comet"
      weight: 0.3
    - name: "dharma_accuracy"
      weight: 0.2
  human_evaluation:
    sangha_review: true
    expert_review: true
    community_feedback: true
  terminology:
    check_consistency: true
    verify_usage: true
    maintain_register: true

# Infrastructure Configuration
infrastructure:
  compute:
    device: "cuda"
    precision: "mixed"
    distributed: true
  logging:
    level: "INFO"
    save_path: "logs/"
  monitoring:
    tensorboard: true
    wandb: true
  checkpointing:
    save_best: true
    save_last: true
    max_checkpoints: 5

High Level Implementation Plan for Fine-Tuning¶

1. Foundation Phase (2-3 weeks)¶

Core data structures & basic pipeline
Minimal preprocessing
Simple model integration
Basic evaluation

2. Rapid Prototype Phase (4-6 weeks)¶

End-to-end workflow
Basic fine-tuning capability
Simple translation testing
Initial quality metrics

3. Enhancement Phase (Ongoing)¶

Advanced features integration
Quality improvements
Specialized capabilities
Production readiness

Intermediate Level Plan¶

1. Foundation Phase¶

a. Data Pipeline (Week 1)¶

Basic corpus loader
Simple text alignment
Minimal preprocessing
Validation scaffolding

b. Model Integration (Week 2)¶

OpenAI API integration
Basic prompt engineering
Simple fine-tuning setup
Test harness creation

c. Basic Infrastructure (Week 3)¶

Configuration management
Logging setup
Experiment tracking
Initial testing framework

2. Rapid Prototype Phase¶

a. Core Workflow (Weeks 4-5)¶

Data loading pipeline
Training loop implementation
Basic evaluation metrics
Result logging

b. Initial Testing (Weeks 6-7)¶

Small-scale fine-tuning
Translation testing
Quality assessment
Performance analysis

c. Pipeline Integration (Weeks 8-9)¶

Workflow automation
Error handling
Basic monitoring
Documentation

3. Enhancement Phase¶

a. Quality Improvements¶

Advanced preprocessing
Better alignment methods
Enhanced validation
Extended metrics

b. Feature Integration¶

Terminology management
Review system
Quality analysis
Advanced monitoring

Detailed Implementation Plan¶

1. Foundation Phase¶

Week 1: Data Pipeline¶

Day 1-2: Corpus Management
- Implement basic ParallelCorpus class
- Create TextUnit data structure
- Build simple file loading
- Set up basic validation

Day 3-4: Text Alignment
- Implement sentence splitting
- Basic alignment matching
- Simple alignment scoring
- Basic quality checks

Day 5: Preprocessing
- Text cleaning functions
- Basic tokenization
- Language detection
- Format standardization

Week 2: Model Integration¶

Day 1-2: API Setup
- OpenAI client setup
- Authentication handling
- Basic API wrapper
- Error handling

Day 3-4: Fine-tuning Basics
- Data formatting
- Basic training loop
- Simple inference
- Result handling

Day 5: Test Framework
- Unit test setup
- Integration tests
- Performance metrics
- Basic logging

Week 3: Infrastructure¶

Day 1-2: Configuration
- YAML config setup
- Environment management
- Parameter handling
- Version control

Day 3-4: Logging & Monitoring
- Logging implementation
- Basic metrics tracking
- Error reporting
- Status monitoring

Day 5: Integration
- Pipeline assembly
- Basic workflow
- Documentation
- Initial testing

2. Rapid Prototype Phase¶

Weeks 4-5: Core Workflow¶

Days 1-3: Pipeline Integration
- Data loading workflow
- Preprocessing pipeline
- Model integration
- Basic evaluation

Days 4-7: Training Implementation
- Fine-tuning loop
- Batch processing
- Checkpointing
- Initial validation

Days 8-10: Quality Control
- Basic metrics
- Result validation
- Error analysis
- Performance tracking

Days 1-5: Initial Testing
- Small dataset testing
- Performance evaluation
- Error analysis
- Pipeline validation

Days 6-10: Refinement
- Workflow optimization
- Error handling
- Performance tuning
- Documentation

Critical Implementation Priorities¶

1. Data Management¶

# Minimal ParallelCorpus implementation
class ParallelCorpus:
    def load_texts(self):
        # Priority 1: Basic file loading
        # Priority 2: Error handling
        # Priority 3: Metadata handling
        pass

    def align_texts(self):
        # Priority 1: Simple matching
        # Priority 2: Quality scoring
        # Priority 3: Advanced alignment
        pass

2. Model Integration¶

# Basic model wrapper
class ModelWrapper:
    def prepare_fine_tuning(self):
        # Priority 1: Data formatting
        # Priority 2: Parameter setup
        # Priority 3: Advanced options
        pass

    def train(self):
        # Priority 1: Basic training loop
        # Priority 2: Monitoring
        # Priority 3: Optimization
        pass

3. Evaluation Framework¶

# Core evaluation
class Evaluator:
    def evaluate_translation(self):
        # Priority 1: Basic metrics
        # Priority 2: Quality analysis
        # Priority 3: Advanced metrics
        pass

    def generate_report(self):
        # Priority 1: Basic statistics
        # Priority 2: Detailed analysis
        # Priority 3: Advanced insights
        pass

Extension Points¶

1. Data Pipeline¶

Advanced text alignment
Enhanced preprocessing
Quality validation
Metadata handling

2. Model Development¶

Advanced fine-tuning
Multiple model support
Hyperparameter optimization
Model ensembling

3. Evaluation¶

Custom metrics
Automated analysis
Review integration
Quality assurance

Note: This implementation plan is intended to provide a path to a working prototype while maintaining extensibility for future enhancements.

Fine Tuning Strategy¶

Initial Prompt¶

Translation Project Structure¶

Core Implementation Classes¶

Training Configuration¶

High Level Implementation Plan for Fine-Tuning¶

1. Foundation Phase (2-3 weeks)¶

2. Rapid Prototype Phase (4-6 weeks)¶

3. Enhancement Phase (Ongoing)¶

Intermediate Level Plan¶

1. Foundation Phase¶

a. Data Pipeline (Week 1)¶

b. Model Integration (Week 2)¶

c. Basic Infrastructure (Week 3)¶

2. Rapid Prototype Phase¶

a. Core Workflow (Weeks 4-5)¶

b. Initial Testing (Weeks 6-7)¶

c. Pipeline Integration (Weeks 8-9)¶

3. Enhancement Phase¶

a. Quality Improvements¶

b. Feature Integration¶

Detailed Implementation Plan¶

1. Foundation Phase¶

Week 1: Data Pipeline¶

Week 2: Model Integration¶

Week 3: Infrastructure¶

2. Rapid Prototype Phase¶

Weeks 4-5: Core Workflow¶

Weeks 6-7: Testing & Refinement¶

Critical Implementation Priorities¶

1. Data Management¶

2. Model Integration¶

3. Evaluation Framework¶

Extension Points¶

1. Data Pipeline¶

2. Model Development¶

3. Evaluation¶