I Trained a Small Language Model from Scratch
4 hours ago
- #AI
- #Small Language Models
- #Business Efficiency
- The AI ecosystem is growing, but large models often fail to deliver ROI, with 42% of projects yielding zero returns.
- Small Language Models (SLMs) offer a specialized, efficient alternative to large, general-purpose models.
- Large models like GPT-4 have high computational costs and struggle with business-specific contexts.
- SLMs (1M-10B parameters) focus on deep specialization, such as a 16M parameter model trained on medical call transcripts.
- A BYOD (Bring Your Own Data) pipeline was built to demonstrate SLM efficiency, using automotive customer service call data.
- The 16M parameter model showed improved training loss (9.2 -> 2.2) and learned domain-specific conversation patterns.
- Advantages of SLMs include memory efficiency (64MB storage), faster inference, and predictable costs.
- SLMs integrate deeply into business systems without requiring architectural overhauls.
- Limitations: SLMs lack general knowledge but excel in focused tasks. Multiple SLMs can be deployed for broader coverage.
- Data quality is critical for SLMs; preprocessing steps include normalizing speaker IDs and removing metadata.
- Managing multiple SLMs requires standardized pipelines, centralized monitoring, and consistent APIs.
- The future of enterprise AI lies in specialized, efficient models rather than large, general-purpose ones.