EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Aug 22·5 min read
Why your local LLM feels dumber than it is
TL;DR
Local implementations of large language models (LLMs) often underperform compared to their reference implementations due to hardware and software discrepancies. This article explores how these differences impact model performance and user experience.
✦ Why It Matters
Evaluate your hardware and software setup to improve LLM performance today.
Key Takeaways
- Run comprehensive benchmarks tailored to your specific use case.
- Be cautious of low KLD claims without full context.
- Understand the impact of different attention backends on performance.
- Quantization can significantly affect model behavior during tool calls.
How It Works
The article explains that LLMs generate text by predicting the next token based on previous context, using logits that are converted into probabilities. The choice of attention backend and quantization method can lead to different outcomes in token predictions, affecting overall model performance.