doc2lora

This repository is a small library for fine-tuning LLMs using LoRA (Low-Rank Adaptation) by using a folder of documents as input. It is designed to be simple and easy to use, allowing users to quickly adapt large language models to specific tasks or domains.

The library allows you to pass a folder of documents (local or from R2 bucket) and turn them into a LoRA Adapter. It is particularly useful for fine-tuning models on domain-specific data, such as legal documents, medical texts, or any other specialized corpus. It is intended to be used with Cloudflare Workers AI or similar platforms that support LLM fine-tuning.

It supports the following formats:

• Markdown: .md files
• Text: .txt files or blank text files
• PDF: .pdf files
• HTML: .html files
• Word Documents: .docx files
• Excel Spreadsheets: .xlsx files
• CSV: .csv files
• JSON: .json files
• YAML: .yaml / .yml files
• XML: .xml files
• LaTeX: .tex files
• Archive Formats: .zip, .tar.gz, tar.xz, etc with supported documents inside

Quick Start

Installation

# Install the package
pip install -e .

# For full functionality with ML training, install additional dependencies:
pip install torch transformers peft datasets

# For additional document format support:
pip install PyPDF2 python-docx beautifulsoup4 PyYAML openpyxl

# For R2 bucket support:
pip install boto3

Basic Usage

# Test the example
cd examples
python basic_usage.py

Library Usage

To use the library, you can import it into your project and call the convert function with the path to the folder containing your documents, or use convert_from_r2 to process documents from an R2 bucket. The library will handle the parsing and conversion of the documents into a format suitable for LoRA fine-tuning.

The convert function now supports multiple input types:

• Folder path: Pass a path to a folder containing documents
• Array of strings: Pass document content directly as strings
• Array of bytes: Pass document content as byte arrays
• Single string: Pass individual document content
• Single bytes: Pass individual document as bytes

Subdirectory-Based Labeling

doc2lora now automatically uses subdirectory structure combined with filenames to create detailed labels, making it easy to organize training data by category.

When processing a folder, each document is automatically labeled by combining its subdirectory and filename:

training_data/
├── legal/              # Documents labeled as "legal_[filename]"
│   ├── contract1.pdf   # -> "legal_contract1"
│   └── agreement.docx  # -> "legal_agreement"
├── technical/          # Documents labeled as "technical_[filename]"
│   ├── spec.md         # -> "technical_spec"
│   └── guide.txt       # -> "technical_guide"
├── marketing/          # Documents labeled as "marketing_[filename]"
│   ├── campaign.html   # -> "marketing_campaign"
│   └── copy.txt        # -> "marketing_copy"
└── overview.txt        # Root-level files → "root_overview"

Generated metadata includes:

{
  "content": "Document content...",
  "filename": "contract1.pdf",
  "label": "legal_contract1",
  "category_path": "legal",
  "extension": ".pdf",
  "size": 1024
}

Use Cases:

• Domain + Document type: legal_contract, legal_agreement, technical_spec, technical_guide
• Difficulty + Topic: beginner_python, intermediate_javascript, advanced_algorithms
• Type + Content: manual_installation, faq_troubleshooting, tutorial_setup
• Language + Region: en_privacy_policy, es_terms_service, fr_user_guide
• Time + Event: 2023_quarterly_report, 2024_annual_summary, current_status

# See the labeling feature in action
cd examples
python subdirectory_labeling_demo.py

Local Documents

from doc2lora import convert

# Method 1: Convert a folder of documents
convert(documents_path="path/to/documents", output_path="path/to/output.json")

# Method 2: Convert array of strings directly
documents = [
    "This is document 1 content...",
    "This is document 2 content...",
    "This is document 3 content..."
]
convert(input_data=documents, output_path="path/to/output.json")

# Method 3: Convert single string
document_content = "This is my document content..."
convert(input_data=document_content, output_path="path/to/output.json")

# Method 4: Convert array of bytes
with open("doc1.txt", "rb") as f1, open("doc2.txt", "rb") as f2:
    byte_documents = [f1.read(), f2.read()]
convert(input_data=byte_documents, output_path="path/to/output.json")

R2 Bucket Documents

from doc2lora import convert_from_r2

# Method 1: Direct credentials
convert_from_r2(
    bucket_name="my-documents-bucket",
    folder_prefix="training-docs",  # optional
    output_path="path/to/output.json",
    aws_access_key_id="your-access-key",
    aws_secret_access_key="your-secret-key",
    endpoint_url="https://your-account.r2.cloudflarestorage.com"
)

# Method 2: Using .env file (recommended)
convert_from_r2(
    bucket_name="my-documents-bucket",
    folder_prefix="training-docs",  # optional
    output_path="path/to/output.json",
    env_file=".env"  # Load credentials from .env file
)

# The output will be a JSON file containing the LoRA adapter data
# You can then use this output with your LLM fine-tuning framework
# For example, with Cloudflare Workers AI:
from cloudflare_workers_ai import LLM
llm = LLM(model="your-model-name")
llm.load_lora_adapter("path/to/output.json")

CLI

You can also use the library from the command line. The CLI allows you to convert a folder of documents or R2 bucket contents into a LoRA adapter JSON file.

CLI for Local Documents

doc2lora convert path/to/documents --output path/to/output.json

CLI for R2 Bucket Documents

# Method 1: Set environment variables for credentials
export R2_ACCESS_KEY_ID="your-access-key"
export R2_SECRET_ACCESS_KEY="your-secret-key"
export R2_ENDPOINT_URL="https://your-account.r2.cloudflarestorage.com"

# Convert documents from R2 bucket
doc2lora convert-r2 my-documents-bucket --folder-prefix training-docs --output path/to/output.json

# Method 2: Use .env file (recommended)
doc2lora convert-r2 my-documents-bucket \
    --env-file .env \
    --folder-prefix training-docs \
    --output path/to/output.json

# Method 3: Pass credentials directly
doc2lora convert-r2 my-documents-bucket \
    --r2-access-key-id "your-access-key" \
    --r2-secret-access-key "your-secret-key" \
    --endpoint-url "https://your-account.r2.cloudflarestorage.com" \
    --output path/to/output.json

Project Structure

doc2lora/
├── doc2lora/           # Main package
│   ├── __init__.py     # Package initialization
│   ├── core.py         # Main convert function
│   ├── parsers.py      # Document parsing logic
│   ├── lora_trainer.py # LoRA training implementation
│   ├── cli.py          # Command-line interface
│   └── utils.py        # Utility functions
├── examples/           # Example usage
│   ├── basic_usage.py  # Working example script
│   ├── subdirectory_labeling_demo.py # Subdirectory labeling demonstration
│   ├── mistral_usage.py # Mistral model example with HF API key
│   ├── gemma_usage.py  # Gemma model example for Cloudflare AI
│   ├── llama_usage.py  # Llama model example for Cloudflare AI
│   ├── r2_usage.py     # R2 bucket integration example
│   └── example_documents/  # Sample documents
│       ├── sample.md
│       ├── sample.txt
│       ├── sample.json
│       └── sample.csv
├── demo/              # Complete working demonstration
│   ├── data/          # Sample training documents about software development
│   ├── scripts/       # Automation scripts (train_lora.sh/.bat, deploy_to_r2.sh/.bat)
│   ├── worker.js      # Cloudflare Worker implementation
│   ├── wrangler.toml  # Cloudflare Worker configuration
│   ├── index.html     # Web interface for testing
│   └── README.md      # Demo documentation
├── tests/             # Test suite
├── requirements.txt   # Dependencies
├── setup.py          # Package setup
└── README.md         # This file

Examples

The examples/ directory contains usage examples for different models and scenarios:

Model-Specific Examples

1. mistral_usage.py - Complete example for Mistral models with HuggingFace authentication

   cd examples
   export HF_API_KEY="your_huggingface_token"  # Required for Mistral models
   python mistral_usage.py

2. gemma_usage.py - Google Gemma model fine-tuning for Cloudflare Workers AI

   cd examples
   python gemma_usage.py

3. llama_usage.py - Meta Llama 2 model fine-tuning with optimized parameters

   cd examples
   python llama_usage.py

4. r2_usage.py - R2 bucket integration with .env file support

   cd examples
   python r2_usage.py

Demo Application

The demo/ folder contains a complete working demonstration of a Cloudflare Worker using a custom LoRA adapter:

# 1. Train a LoRA adapter on software development data
cd demo
./scripts/train_lora.sh  # or train_lora.bat on Windows

# 2. Deploy the adapter to R2 bucket
./scripts/deploy_to_r2.sh  # or deploy_to_r2.bat on Windows

# 3. Deploy the Cloudflare Worker
./scripts/wrangler_deploy.sh  # or wrangler_deploy.bat on Windows

The demo creates a Software Developer Assistant AI that provides guidance on:

• Code development and architecture
• Debugging and troubleshooting
• Team collaboration and communication
• Professional growth and career development
• Technical decision-making

API Endpoints:

• GET /health - Health check
• POST /chat - Send message and get response
• POST /chat/stream - Streaming responses
• GET /docs - API documentation

Configuration

GPU Support

🚀 Automatic GPU Detection: doc2lora now automatically detects and uses the best available device for training:

Device Priority (Automatic):

1. 🚀 NVIDIA GPU (CUDA) - Fastest training with fp16 precision and optimal memory usage
2. 🍎 Apple Silicon (MPS) - Good performance on Mac M1/M2/M3
3. 💻 CPU - Reliable fallback, works everywhere

Automatic Detection (Recommended):

# Will automatically use GPU if available, fallback to CPU
doc2lora convert ./docs --output adapter.json

Manual Device Selection:

# Force GPU usage
doc2lora convert ./docs --output adapter.json --device cuda

# Force CPU usage (useful for troubleshooting)
doc2lora convert ./docs --output adapter.json --device cpu

# Use Apple Silicon GPU (Mac M1/M2/M3)
doc2lora convert ./docs --output adapter.json --device mps

Python API:

from doc2lora import convert

# Auto-detect device (recommended)
convert(documents_path="./docs", output_path="adapter.json")

# Specify device manually
convert(documents_path="./docs", output_path="adapter.json", device="cuda")
convert(documents_path="./docs", output_path="adapter.json", device="cpu")
convert(documents_path="./docs", output_path="adapter.json", device="mps")  # Apple Silicon

GPU Requirements:

• NVIDIA GPUs: Requires CUDA-compatible PyTorch installation
• Apple Silicon: Requires PyTorch with MPS support (automatically included on macOS)
• Memory: 8GB+ GPU memory recommended for larger models

Training Parameters

Common configuration options:

doc2lora convert ./docs \
    --model mistralai/Mistral-7B-Instruct-v0.2 \
    --batch-size 2 \
    --epochs 3 \
    --learning-rate 2e-4 \
    --lora-r 8 \
    --lora-alpha 16 \
    --device auto  # or cuda/mps/cpu

Memory Management:

• 🚀 GPU Training: Automatically uses fp16 precision on CUDA GPUs to save memory
• 🔧 Out of Memory: Reduce --batch-size if you encounter GPU memory errors
• 💻 CPU Fallback: Use --device cpu if GPU memory is insufficient
• ⚡ Automatic Optimization: The system automatically chooses optimal settings per device

Features

• ✅ Document Parsing: Recursively scan directories for supported document types
• ✅ Subdirectory Labeling: Automatically label documents based on directory structure and filename
• ✅ Multiple Formats: Support for 16+ document formats including archives
• ✅ Archive Support: Extract and parse documents from ZIP and TAR archives
• ✅ R2 Bucket Support: Direct integration with Cloudflare R2 storage buckets
• ✅ CLI Interface: Easy-to-use command-line interface
• ✅ Flexible Configuration: Customizable LoRA parameters
• 🔄 LoRA Training: Fine-tune models using LoRA adaptation (requires ML dependencies)
• 🔄 Export Options: JSON format compatible with various platforms

Status

• Document Parsing: ✅ Fully working
• CLI Interface: ✅ Basic functionality working
• LoRA Training: 🔄 Requires ML dependencies (torch, transformers, peft, datasets)

The core document parsing functionality works out of the box. For full LoRA training capabilities, install the ML dependencies listed above.