EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Sep 1·5 min read
44% on ARC-AGI-1 in 67 cents
TL;DR
A small transformer model was trained from scratch in just 1.5 hours on a 5090 GPU, achieving a 44% score on the ARC-AGI-1 benchmark. This work aims to explore the limits of sample efficiency in AI and reduce training costs.
✦ Why It Matters
Consider experimenting with small transformer models to enhance sample efficiency in your AI projects.
Key Takeaways
- Experiment with the open-source transformer model to enhance your AI projects.
- Focus on sample efficiency to reduce training costs and improve performance.
- Consider using modern architectures like SwiGlu and RMSnorm for better results.
- Explore data augmentation techniques to optimize model training without increasing costs.
How It Works
The model converts input-output pairs into token sequences, which are autoregressively trained. Each puzzle is assigned a unique embedding to facilitate cross-task learning, and sequences are augmented with color and dihedral permutations to enhance performance during inference.