Estimating Black-Box LLM Parameter Counts via Factual Capacity

Researchers have developed a technique to estimate the parameter count of black-box LLMs through factual capacity testing, without requiring access to model weights or architecture details. This approach uses probing questions and response analysis to infer model size—valuable for practitioners evaluating models before deployment.

For local LLM operators, this technique simplifies model selection and benchmarking. When evaluating an unfamiliar quantised model or a variant from an OSS community, you can now estimate its effective capacity without manual inspection. This is particularly useful for understanding trade-offs when using quantised versions in llama.cpp or Ollama, ensuring you select the right model size for your hardware constraints.

Explore the arXiv paper to learn how factual capacity analysis can inform your deployment strategy and help you compare models more accurately across different optimization levels.

Source: Hacker News · Relevance: 8/10