Multimodal Llama-2 Models: Latency-Accuracy Trade-offs in Diagram-Based Code Generation

This report synthesises findings from 11 peer-reviewed papers addressing the following research question: What is the trade-off between inference latency and accuracy when deploying Llama-2 multimodal models for diagram-based code generation, as measured by pass@1 and throughput on HumanEval-V. In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and. 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.2/10. 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 accuracy when deploying Llama-2 multimodal models for diagram-based code generation, as measured by pass@1 and throughput on HumanEval-V?

Autonomous literature synthesis. Automated review score: 9.2/10. Full text and citation available at Assignee Research.