doc2lora 1.0.2

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 via Whisper): .wav, .mp3, .m4a, .flac, .aac, .ogg, and more
• Images (OCR text recognition): .png, .jpg, .bmp, .gif, .tiff, .webp, and more; .svg text is read from the markup
• Video (audio transcript + on-screen-text OCR): .mp4, .avi, .mov, .mkv, .webm, 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, rtf, epub, xlsx, 7z
pip install "doc2lora[image]"   # image OCR (needs the system tesseract-ocr binary)
pip install "doc2lora[audio]"   # speech-to-text via Whisper (needs the ffmpeg binary)
pip install "doc2lora[video]"   # video: per-frame OCR + audio transcript
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]"

System dependencies - the image/audio/video extras shell out to native binaries that pip can't install:

Feature	Needs	macOS (Homebrew)	Debian/Ubuntu	Fedora
Image / video OCR ([image], [video])	tesseract-ocr	brew install tesseract	sudo apt-get install tesseract-ocr	sudo dnf install tesseract
Audio / video transcription ([audio], [video])	ffmpeg	brew install ffmpeg	sudo apt-get install ffmpeg	sudo dnf install ffmpeg

opencv-python bundles its own libraries in the wheel (no system package), and ODT/ODS and SVG are parsed with the stdlib (no extra or system binary). Audio/video transcription defaults to faster-whisper (falls back to openai-whisper, then SpeechRecognition); choose a backend with --audio-backend and a model size with --whisper-model. For more OCR languages, install the tesseract language pack (e.g. brew install tesseract-lang, sudo apt-get install tesseract-ocr-fra) and pass --ocr-languages eng+fra.

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 + per-file and total training-time estimates
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 init + single-source __version__
│   ├── core.py           # convert() / convert_from_r2() entry points
│   ├── parsers.py        # Document / image / audio / video parsing
│   ├── lora_trainer.py   # LoRA training, device/precision, speedups
│   ├── deploy.py         # Upload adapters to Cloudflare Workers AI
│   ├── cli.py            # Command-line interface
│   └── utils.py          # R2 download + helpers
├── examples/             # Example usage scripts
│   ├── basic_usage.py
│   ├── media_and_optimization.py   # images/audio/video + speed knobs
│   ├── mistral_usage.py            # Mistral (needs HF_API_KEY)
│   ├── gemma_usage.py              # Gemma
│   ├── llama_usage.py              # Llama 2
│   ├── qwq_usage.py                # QwQ-32B (4-bit QLoRA)
│   ├── qlora_usage.py              # 4-bit QLoRA + deploy
│   ├── r2_usage.py                 # R2 bucket integration
│   ├── subdirectory_labeling_demo.py
│   └── example_documents/          # Sample documents
├── demo/                 # Complete Cloudflare Workers AI demo
│   ├── data/             # Sample training corpus
│   ├── scripts/          # train_lora.sh/.bat, deploy_to_r2.sh/.bat
│   ├── worker.js         # Worker (loads adapter, /chat endpoints)
│   ├── wrangler.toml     # Worker configuration
│   ├── index.html        # Browser UI
│   └── README.md         # Demo documentation
├── tests/                # Test suite (pytest)
├── pyproject.toml        # Packaging, dependencies/extras, tool config
├── requirements.txt      # Full install (equivalent to the [all] extra)
├── setup.py              # Thin shim (metadata lives in pyproject.toml)
├── README.md             # This file
├── USAGE.md              # Usage guide
├── INSTALL_GUIDE.md      # Install + Mistral guide
└── CLAUDE.md             # Repo guide for Claude Code

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
   

7. media_and_optimization.py - Ingest images / audio / video and tune the training-speed knobs (the auto defaults plus the opt-in flags)

   cd examples
   python media_and_optimization.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; bf16 on Ampere+ (else fp16), TF32 matmul, and fused AdamW
2. 🍎 Apple Silicon (MPS) - Good performance on Mac M1/M2/M3 (bf16 on macOS 14+, else fp32; fp16 autocast on MPS is NaN-prone and is never auto-enabled)
3. 💻 CPU - Reliable fallback, works everywhere (fp32)

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 bf16-capable CUDA and Apple MPS (macOS 14+), fp16 on other CUDA GPUs, fp32 on CPU and older MPS (fp16 autocast on MPS is NaN-prone).
• 💻 Out of Memory: reduce --batch-size, raise --gradient-accumulation-steps, or fall back with --device cpu (CUDA OOM also auto-falls back to CPU).

Training speed optimizations

doc2lora applies a set of hardware-aware speedups automatically - most are no-ops where they don't apply - and exposes a few opt-in ones for power users.

Applied automatically:

Optimization	What it does	Where it helps
TF32 matmul (set_float32_matmul_precision("high"))	runs fp32 matmuls on Tensor Cores	NVIDIA Ampere+ (no-op on older CUDA / MPS / CPU)
bf16 / fp16 precision	bf16 on bf16-capable CUDA & MPS (macOS 14+), fp16 on other CUDA, fp32 elsewhere	CUDA, Apple Silicon
Fused AdamW (optim="adamw_torch_fused")	single fused optimizer kernel	CUDA with PyTorch >= 2.8 (else plain AdamW)
SDPA attention	PyTorch scaled-dot-product attention auto-selects the fastest kernel	all (CUDA fused kernels; math fallback on CPU/MPS)
pad_to_multiple_of=8	aligns padded batches to Tensor-Core tiles	CUDA (harmless elsewhere)
CUDA-gated pinned memory	dataloader_pin_memory only on CUDA	avoids wasted host RAM on CPU/MPS
Gradient checkpointing (use_reentrant=False)	recompute activations to save memory	low-RAM machines (on by default; ~20% slower - disable with --no-gradient-checkpointing if you have memory headroom)
torch.compile	fuses the model graph (~20-50% faster steps)	auto on CUDA when the corpus is >= ~10 MB of text (compile cost amortizes on long runs only; CUDA-only). Force with --torch-compile / --no-torch-compile
Length-grouped batching	groups similar-length samples to cut padding	auto when batch_size >= 2 (hardware-agnostic; nothing to cut at batch 1). Force with --group-by-length / --no-group-by-length
Parallel parsing	thread pool over the document folder (PDF / OCR / transcription)	auto: ~min(8, CPU count) threads; tune with --max-workers N

Opt-in (you choose when it's worth it):

Flag	What it does	When to use
--no-torch-compile	force torch.compile off (it auto-enables on CUDA for large corpora)	short runs, debugging, or if compile graph-breaks
--no-group-by-length	force length-grouped batching off (it auto-enables at batch >= 2)	if you need strict shuffle order or hit a convergence quirk
--attn-implementation flash_attention_2	FlashAttention-2 kernel	Ampere+ CUDA with flash-attn + bf16/fp16 (falls back to eager; SDPA already uses flash kernels by default)
--optim adamw_bnb_8bit	8-bit Adam (~75% less optimizer memory)	full fine-tuning on CUDA; little benefit for LoRA (optimizer state is just the tiny adapter) - needs [quant]/bitsandbytes
--dataloader-num-workers N	extra DataLoader worker processes	large corpora on Linux/CUDA only (default 0; keep 0 on macOS / in-memory data)

Per-platform notes:

• NVIDIA CUDA: prefer bf16 on Ampere+ (no loss scaling, no NaNs); TF32, the fused optimizer, and (for corpora >= ~10 MB of text) torch.compile are on automatically. For very long sequences, --attn-implementation flash_attention_2 (with pip install flash-attn) is the biggest single win.
• Apple Silicon (MPS): bf16 is used on macOS 14+ (same memory as fp16, but stable), else fp32. torch.compile, FlashAttention, and 4-bit QLoRA do not help on MPS; length-grouped batching (auto at batch_size >= 2) is the main extra lever that does. Keep the whole model in unified memory (no CPU offload exists on MPS).
• CPU: training is a slow fallback. PyTorch already defaults to your physical-core count; you can pin it with OMP_NUM_THREADS. Use a small base model.

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 bf16 LoRA (macOS 14+, else fp32) 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	10 MB +optimizations†	100 MB	100 MB +optimizations†
Apple M2	8-24 GB unified	bf16 LoRA (16 GB+ for 7B)	~1 hr	~11 hrs	n/a (CUDA only)	~4-5 days	n/a (CUDA only)
Apple M3	8-128 GB unified	bf16 LoRA	~40 min	~6 hrs	n/a	~2-3 days	n/a
Apple M4	16-128 GB unified	bf16 LoRA	~25 min	~4 hrs	n/a	~1.5 days	n/a
RTX 4070	12 GB	QLoRA (4-bit) required	~10 min	~1.5 hrs	~1 hr	~17 hrs	~12 hrs
RTX 5070	12 GB	QLoRA (4-bit) required	~7 min	~1.2 hrs	~50 min	~12 hrs	~8-9 hrs
RTX 3090	24 GB	full bf16 LoRA	~7 min	~1 hr	~40 min	~11 hrs	~7-8 hrs
RTX 4090	24 GB	full bf16 LoRA	~4 min	~35 min	~25 min	~6 hrs	~4 hrs
RTX 5090	32 GB	full bf16 LoRA	~2 min	~20 min	~15 min	~3-4 hrs	~2-3 hrs

† +optimizations = the dynamic speedups doc2lora turns on for you, on top of the always-on defaults (TF32, fused AdamW, bf16, SDPA, pad_to_multiple_of=8 - already in the base columns). For these CUDA rows it is dominated by torch.compile (auto on CUDA once the corpus is >= ~10 MB of text; ~20-40% faster steps per HuggingFace, the column applies ~30%) plus length-grouped batching (auto at batch_size >= 2). The compile cost (seconds to minutes) only amortizes on long runs, so the 10 MB and 100 MB columns reflect it while the ~1 MB / minutes-scale column does not. Override with --torch-compile / --no-torch-compile (and --group-by-length). It does not help on Apple MPS - compile and FlashAttention are CUDA-only, and 7B on Apple runs at batch_size 1 (no length-grouping win), so Apple's gains are the bf16/etc. already in the base columns. For long sequences on Ampere+ GPUs, stack --attn-implementation flash_attention_2 (needs pip install flash-attn).

What counts as an "example"? Each document becomes one or more training examples: a file <= --max-length tokens (default 512, ~2 KB of text) is one example, and a longer file is auto-chunked into consecutive --max-length-token windows - one example each - so all of its content is trained on (use --chunk-overlap N to overlap windows, or --no-chunk to revert to truncating each file to its first window). So "a few hundred to a few thousand examples" is really total tokens / max_length.

How this impacts performance: training time scales with total tokens, so chunking a few huge files can balloon the run - a dozen 1 MB files is ~6,000 chunks (~500x more steps than the old truncate-to-12-examples behavior). Bound it with --max-steps, fewer --epochs, a smaller/curated corpus, or --no-chunk. Curated quality still beats raw quantity. doc2lora scan estimates time from total bytes - a rough figure, but its all-content basis now matches what's actually trained (chunking on); under the old truncation default it over-counted for big files. 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 20+ document, image, audio, and video 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
• ✅ Image / Audio / Video: OCR images (tesseract), transcribe audio & video with Whisper, and OCR on-screen text from frames
• ✅ Parallel Parsing: multithreaded parsing of the document folder
• ✅ Hardware-aware Speedups: TF32, bf16, fused AdamW, SDPA, and auto torch.compile / length-grouped batching selected by device + corpus size
• ✅ One-command Deploy: doc2lora deploy uploads adapters to Cloudflare Workers AI (wrangler or REST)
• ✅ 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.