EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Sep 10·5 min read
Training a 3.8B LLM to 0.384 CORE for $998 – Hugo Vergnes
TL;DR
A 3.8 billion-parameter language model was successfully trained for $998, achieving a CORE score of 0.384 after processing 65 billion tokens in just 43 hours.
✦ Why It Matters
Consider experimenting with small-scale LLM training to explore AI model development without significant investment.
Key Takeaways
- Utilize a config-driven framework for easier model experimentation.
- Optimize learning rate schedules to enhance model training efficiency.
- Consider using FP8 training for improved throughput.
- Select high-quality datasets to accelerate convergence.
Full Summary
Hugo Vergnes trained a 3.8 billion parameter language model, named little-lm, using a cost-effective approach that involved renting GPUs and leveraging a config-driven framework. The model was trained on 65 billion tokens over 43 hours, achieving a CORE score of 0.384, which is significantly higher than previous models like nanochat d32.
Key improvements included optimizing the learning rate schedule, using a more effective dataset, and employing FP8 training for better throughput. The project highlights the potential for individual developers to create competitive AI models without extensive resources, emphasizing the importance of good infrastructure and configuration management in AI development.
The findings suggest that with careful tuning, smaller teams can achieve results comparable to larger organizations.