papertuner 0.0.1

A package for creating ML research assistant models through paper dataset creation and model fine-tuning

PaperTuner

PaperTuner is a Python package for creating research assistant models by processing academic papers and fine-tuning language models to provide methodology guidance and research approaches.

Features

• Automated extraction of research papers from arXiv
• Section extraction to identify problem statements, methodologies, and results
• Generation of high-quality question-answer pairs for research methodology
• Fine-tuning of language models with GRPO (Growing Rank Pruned Optimization)
• Integration with Hugging Face for dataset and model sharing

Installation

pip install papertuner

Basic Usage

As a Command-Line Tool

1. Create a dataset from research papers

# Set up your environment variables
export GEMINI_API_KEY="your-api-key"
export HF_TOKEN="your-huggingface-token"  # Optional, for uploading to HF

# Run the dataset creation
papertuner-dataset --max-papers 100

2. Train a model

# Train using the created or an existing dataset
papertuner-train --model "Qwen/Qwen2.5-3B-Instruct" --dataset "densud2/ml_qa_dataset"

As a Python Library

from papertuner import ResearchPaperProcessor, ResearchAssistantTrainer

# Create a dataset
processor = ResearchPaperProcessor(
    api_key="your-api-key",
    hf_repo_id="your-username/dataset-name"
)
papers = processor.process_papers(max_papers=10)

# Train a model
trainer = ResearchAssistantTrainer(
    model_name="Qwen/Qwen2.5-3B-Instruct",
    lora_rank=64,
    output_dir="./model_output"
)
results = trainer.train("your-username/dataset-name")

# Test the model
question = "How would you design a transformer model for time series forecasting?"
response = trainer.run_inference(
    results["model"],
    results["tokenizer"],
    question,
    results["lora_path"]
)
print(response)

Configuration

You can configure the tool using environment variables or when initializing the classes:

• GEMINI_API_KEY: API key for generating QA pairs
• HF_TOKEN: Hugging Face token for uploading datasets and models
• HF_REPO_ID: Hugging Face repository ID for the dataset
• PAPERTUNER_DATA_DIR: Custom directory for storing data (default: ~/.papertuner/data)

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.