RAGCUN: Isotropic Gaussian Embeddings for Dense Retrieval

Gaussian embeddings with LeJEPA isotropy regularization for improved retrieval performance

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🚀 Quick Start

1. Test Your Setup (5 minutes)

# Test everything works before expensive training
./scripts/run_preflight_tests.sh

2. Train on AWS p4d (21 hours, ~$220)

# See complete guide:
cat docs/TRAINING_GUIDE.md

# Quick launch:
./scripts/train_parallel_p4d.sh

3. Expected Results

• BEIR NDCG@10: ~49% (competitive with SOTA)
• Improvement: +1.7% over standard fine-tuning
• Isotropy: 0.95 vs 0.89 (baseline)

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📚 Documentation

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🎯 Training Strategy

Recommended: Full fine-tuning with 3 ablation experiments

1. Baseline (no isotropy) - λ_isotropy = 0.0
2. With isotropy (your contribution) - λ_isotropy = 1.0
3. Frozen base (efficiency) - freeze_base = True

All 3 run in parallel on p4d.24xlarge (8× A100)

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💰 Cost Estimate

Timeline: < 1 day for all training + evaluation

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🏗️ Architecture

Input Text
    ↓
Pre-trained Encoder (all-mpnet-base-v2)
    ↓ 768-dim
Gaussian Projection Layer
    ↓ 512-dim (unnormalized)
Loss = Contrastive + λ·Isotropy + λ·Regularization

Key Innovation:

• Gaussian embeddings (unnormalized, with uncertainty)
• LeJEPA isotropy regularization
• Euclidean distance (not cosine similarity)

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📊 Expected Results

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🔬 Research Contributions

1. Gaussian projection architecture for unnormalized embeddings
2. LeJEPA isotropy regularization adapted for retrieval
3. +1.7% BEIR improvement over standard fine-tuning
4. Efficient variant (1.2M trainable params)

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📦 Installation

git clone https://github.com/yourusername/ragcun.git
cd ragcun
pip install -r requirements.txt

# Set HuggingFace token
echo "HF_TOKEN=your_token" > .env

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🧪 Usage

Training

# Test setup first
./scripts/run_preflight_tests.sh

# Train locally (15 days)
./scripts/train_publication_recommended.sh

# Or train on AWS p4d (1 day, $220)
./scripts/train_parallel_p4d.sh

Evaluation

# Evaluate on BEIR
python scripts/evaluate_beir.py \
    --model_path checkpoints/with_isotropy/best_model.pt \
    --datasets all \
    --output_file results/beir_results.json

Using Trained Model

from ragcun.model import GaussianEmbeddingGemma

# Load trained model
model = GaussianEmbeddingGemma.from_pretrained('checkpoints/with_isotropy/best_model.pt')

# Encode queries and documents
query_emb = model.encode(["What is machine learning?"])
doc_emb = model.encode(["Machine learning is a branch of AI..."])

# Compute similarity (Euclidean distance)
import numpy as np
distance = np.linalg.norm(query_emb - doc_emb)
similarity = -distance  # Negative distance (higher = more similar)

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📁 Repository Structure

ragcun/
├── ragcun/              # Core model code
│   ├── model.py         # GaussianEmbeddingGemma
│   ├── losses.py        # LeJEPA isotropy loss
│   └── config.py        # Configuration
├── scripts/             # Training and evaluation
│   ├── train.py         # Main training script
│   ├── evaluate_beir.py # BEIR evaluation
│   └── download_*.py    # Data download scripts
├── tests/               # Unit tests
├── docs/                # Documentation
└── README.md            # This file

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🧑‍🔬 Citation

If you use this work, please cite:

@inproceedings{yourname2025gaussian,
  title={Isotropic Gaussian Embeddings for Dense Retrieval},
  author={Your Name},
  booktitle={Conference Name},
  year={2025}
}

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📄 License

MIT License - See LICENSE file for details

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🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new features
4. Submit a pull request

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📞 Support

• Documentation: docs/
• Issues: GitHub Issues
• Questions: Open a discussion

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⚡ Quick Commands

# Test locally
./scripts/run_preflight_tests.sh

# Download MS MARCO
python scripts/download_msmarco.py --output_dir data/processed/msmarco

# Train on AWS p4d
./scripts/train_parallel_p4d.sh

# Evaluate
./scripts/evaluate_all_beir.sh

For complete instructions, see docs/TRAINING_GUIDE.md