Why do we benchmark quants on perplexity and prose but never on tool call validity?
A Reddit discussion on the r/LocalLLaMA subreddit questions the common practice of benchmarking quantized large language models (LLMs) solely on perplexity and prose quality. The user suggests that these metrics may not accurately reflect a model's performance in structured tasks like tool call validity, where even minor quantization errors could lead to fatal failures in generating correct JSON or adhering to function schemas. The post calls for benchmarks that specifically measure the acceptance rate of valid tool calls across different quantization levels, arguing that agentic applications might require lower quantization levels than currently assumed based on text-based evaluations. AI
IMPACT Suggests current LLM quantization benchmarks may be insufficient for agentic applications, potentially impacting the practical deployment of quantized models.