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deepmind.google·6d ago
AI Engineering
Hacker News·23d ago·5 min read
I trained a 113M-parameter earthquake LLM from absolute scratch
TL;DR
A 113M-parameter language model (LLM) for earthquake science was built from scratch, addressing the need for specialized AI in this field. The process involved data crawling, cleaning, tokenization, and training on NVIDIA A30 GPUs.
✦ Why It Matters
Engineers can replicate this process to create tailored LLMs for other specialized domains, enhancing their research capabilities.
Key Takeaways
- Utilize multiple free data sources for model training.
- Implement Byte Pair Encoding for effective tokenization.
- Leverage NVIDIA A30 GPUs for efficient model training.
- Document the entire LLM lifecycle for future reference.
Full Summary
Creating a specialized language model for earthquake science involved several key steps: data crawling from six free sources, cleaning and deduplication, and tokenization using a 16k Byte Pair Encoding (BPE) vocabulary. The model architecture utilized a 113M parameter GQA+RoPE decoder, trained using a two-GPU Distributed Data Parallel (DDP) setup on NVIDIA A30 GPUs.
The training process was designed to optimize performance for streaming inference, allowing the model to respond to earthquake-related queries effectively. The entire lifecycle from data collection to model deployment was documented, providing a comprehensive guide for similar projects.
This work demonstrates the feasibility of developing domain-specific LLMs, which can significantly enhance research and applications in specialized fields like earthquake science.