doc2lora 1.0.0

A library for fine-tuning LLMs using LoRA by using a folder of documents as input

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 / reStructuredText: .md, .rst files
• Text: .txt files or blank text files
• PDF: .pdf files
• HTML: .html files
• Word Documents: .docx files
• PowerPoint: .pptx files (slide text + speaker notes)
• OpenDocument: .odt, .ods files
• Rich Text: .rtf files
• EPUB e-books: .epub files
• Excel Spreadsheets: .xlsx files
• CSV: .csv files
• JSON: .json files
• Jupyter notebooks: .ipynb files (markdown + code cells)
• YAML: .yaml / .yml files
• XML: .xml files
• LaTeX: .tex files
• Source code (read as plaintext): .py, .js, .ts, .java, .kt, .rs, .c/.cpp, .go, .rb, .php, .swift, .dart, .scala, and more
• Audio (speech-to-text): .wav, .mp3, .m4a, .flac, .aac, .ogg, and more
• Archive Formats: .zip, .tar.gz, .tar.xz, .7z, single-file .gz/.bz2/.xz, etc with supported documents inside

Run doc2lora formats to print the full list at any time.

Quick Start

Installation

# Core install (training only):
pip install doc2lora

# Everything (all document formats, audio, R2, QLoRA):
pip install "doc2lora[all]"

# Or pick what you need via extras:
pip install "doc2lora[docs]"    # pdf, docx, pptx, odt/ods, rtf, epub, xlsx, 7z
pip install "doc2lora[audio]"   # speech-to-text (also needs the ffmpeg binary for mp3/m4a/aac)
pip install "doc2lora[r2]"      # Cloudflare R2 ingestion
pip install "doc2lora[quant]"   # 4-bit QLoRA (CUDA only)

# For local development (editable + dev tools):
pip install -e ".[all,dev]"

Audio transcription uses the SpeechRecognition library (Google Web Speech by default, which needs network access). Non-WAV formats are converted with pydub, which requires the system ffmpeg binary.

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

# scan first to preview files + a rough training-time estimate
doc2lora scan path/to/documents --device cpu

# low-memory machine: smaller batch + gradient accumulation (on by default:
# gradient checkpointing). 4-bit QLoRA is available on CUDA via --load-in-4bit
doc2lora convert path/to/documents \
    --batch-size 1 --gradient-accumulation-steps 8 \
    --output adapter.json

Deploy to Cloudflare Workers AI

Once you have an adapter, upload it as a Workers AI finetune with one command:

# uses the wrangler CLI under the hood (validates the adapter first)
doc2lora deploy adapter.json my-finetune-name \
    --cf-model "@cf/mistralai/mistral-7b-instruct-v0.2-lora"

# or upload via the REST API (no wrangler needed)
doc2lora deploy adapter.json my-finetune-name --backend rest \
    --account-id "$CLOUDFLARE_ACCOUNT_ID" --api-token "$CLOUDFLARE_API_TOKEN"

Then reference it at inference time with the lora parameter (env.AI.run("@cf/mistralai/mistral-7b-instruct-v0.2-lora", { ..., lora: "my-finetune-name" })).

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
   

5. qlora_usage.py - Memory-efficient 4-bit QLoRA training (CUDA) + deploy

   cd examples
   python qlora_usage.py
   

6. qwq_usage.py - Fine-tuning the QwQ-32B reasoning model (@cf/qwen/qwq-32b) with 4-bit QLoRA; needs a 24 GB+ NVIDIA GPU

   cd examples
   python qwq_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 \
    --gradient-accumulation-steps 4 \
    --device auto  # or cuda/mps/cpu

LoRA rank: the default is 8 (broadest compatibility). Cloudflare Workers AI now accepts adapters up to rank 32 (with a 300MB safetensors limit), so you can raise --lora-r up to 32 for more capacity; doc2lora only warns above 32.

Performance / low-resource options:

• ⚡ Gradient checkpointing (on by default): trades ~20% compute for a large memory saving. Disable with --no-gradient-checkpointing.
• 🧮 Gradient accumulation: --gradient-accumulation-steps N emulates a larger effective batch (batch_size * N) without the memory cost - ideal on weak machines.
• 🪶 4-bit QLoRA: --load-in-4bit (CUDA + pip install "doc2lora[quant]") loads the base model in 4-bit (nf4) so large models fit on small GPUs.
• 🚀 Precision: bf16 on capable CUDA hardware, fp16 on other GPUs, fp32 on CPU.
• 💻 Out of Memory: reduce --batch-size, raise --gradient-accumulation-steps, or fall back with --device cpu (CUDA OOM also auto-falls back to CPU).

How long will training take?

All numbers below are order-of-magnitude estimates and vary widely with sequence length, batch size, LoRA rank, and data shape. doc2lora scan <dir> --device <d> prints an estimate for your own corpus.

Small base model (DialoGPT-small / GPT-2 class), 3 epochs

Corpus size	CPU	Apple MPS	NVIDIA CUDA
~1 MB	minutes	~1 min	seconds
~10 MB	~1 hour	~10 min	~2 min
~100 MB	many hrs	~1-2 hrs	~20 min

7B-class model (Mistral / Gemma / Llama) vs hardware and VRAM

Times below are for 3 epochs at ~512-token sequences. The "approach" column reflects what fits in memory:

• >= 24 GB VRAM: full fp16/bf16 LoRA fits comfortably.
• 12 GB VRAM: use 4-bit QLoRA (--load-in-4bit) to fit a 7B model.
• Apple Silicon: 4-bit QLoRA is CUDA-only (bitsandbytes), so MPS runs fp16 LoRA and needs ~18 GB+ unified memory for a 7B model; 8 GB Macs cannot train 7B (use a smaller base model). MPS is also much slower than a discrete GPU.

Hardware	Memory	7B approach	1 MB	10 MB	100 MB
Apple M2	8-24 GB unified	fp16 LoRA (16 GB+ for 7B)	~1 hr	~11 hrs	~4-5 days
Apple M3	8-128 GB unified	fp16 LoRA	~40 min	~6 hrs	~2-3 days
Apple M4	16-128 GB unified	fp16 LoRA	~25 min	~4 hrs	~1.5 days
RTX 4070	12 GB	QLoRA (4-bit) required	~10 min	~1.5 hrs	~17 hrs
RTX 5070	12 GB	QLoRA (4-bit) required	~7 min	~1.2 hrs	~12 hrs
RTX 3090	24 GB	full fp16 LoRA	~7 min	~1 hr	~11 hrs
RTX 4090	24 GB	full fp16 LoRA	~4 min	~35 min	~6 hrs
RTX 5090	32 GB	full fp16 LoRA	~2 min	~20 min	~3-4 hrs

For LoRA you usually get better results from a few hundred to a few thousand curated examples than from a huge corpus - data quality beats data quantity. The small-model table above is ~20-40x faster if you only need a lightweight adapter.

32B-class model (QwQ-32B) vs hardware and VRAM

QwQ-32B (@cf/qwen/qwq-32b) also accepts BYO LoRA adapters. A 32B base is roughly 4-5x slower than 7B and only fits with 4-bit QLoRA, which needs ~20-24 GB of VRAM - so it is realistically a 24 GB+ NVIDIA job. Times are for 3 epochs at ~512-token sequences (see examples/qwq_usage.py).

Hardware	Memory	32B approach	1 MB	10 MB	100 MB
Apple M2/M3/M4	unified	not practical (no 4-bit)	-	-	-
RTX 4070 / 5070	12 GB	too small for 32B	-	-	-
RTX 3090	24 GB	QLoRA (4-bit), tight	~30 min	~4.5 hrs	~2 days
RTX 4090	24 GB	QLoRA (4-bit)	~18 min	~2.5 hrs	~1 day
RTX 5090	32 GB	QLoRA (4-bit), roomy	~9 min	~1.5 hrs	~15 hrs

A rank-8..32 adapter on a 32B model is still well under Cloudflare's 300 MB safetensors limit. doc2lora tags Qwen/QwQ adapters with model_type: qwen automatically; deploy with --cf-model "@cf/qwen/qwq-32b".

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.