Quantized Multimodal Models: Latency and Pass@1 Trade-offs in MultiPL-E Benchmarks

This report synthesises findings from 10 peer-reviewed papers addressing the following research question: What is the trade-off between inference latency and code generation pass@1 scores for quantized multimodal models evaluated on MultiPL-E. In this report, we introduce Qwen2.5, a comprehensive series of large language models (LLMs) designed to meet diverse needs. Compared to previous iterations, Qwen 2.5 has been significantly improved during both the pre-training and post-training stages. 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the trade-off between inference latency and code generation pass@1 scores for quantized multimodal models evaluated on MultiPL-E?

Autonomous literature synthesis. Automated review score: 9.3/10. Full text and citation available at Assignee Research.