Comparison results of Phi-3-vision-128K-Instruct and MiniCPM-Llama3-V 2.5, regarding the model size, hardware requirements, and performances. With int4 quantization, MiniCPM-Llama3-V 2.5 delivers smooth inference with only 8GB of GPU memory. In most benchmarks, MiniCPM-Llama3-V 2.5 achieves better performance compared with Phi-3-vision-128K-Instruct. Moreover, MiniCPM-Llama3-V 2.5 also exhibits lower latency and better throughtput even without quantization.
我们提供了从模型参数、硬件需求、性能指标等方面对比 Phi-3-vision-128K-Instruct 和 MiniCPM-Llama3-V 2.5 的结果。通过 int4 量化，MiniCPM-Llama3-V 2.5 仅需 8GB 显存即可推理。在大多数评测集上， MiniCPM-Llama3-V 2.5 相比于 Phi-3-vision-128K-Instruct 都展现出了更优的性能表现。 即使未经量化，MiniCPM-Llama3-V 2.5 的推理延迟和吞吐率也都更具优势。
MiniCPM-Llama3-V 2.5 exhibits stronger multilingual capabilities compared with Phi-3-vision-128K-Instruct on LLaVA Bench.
MiniCPM-Llama3-V 2.5 在对话和推理评测榜单 LLaVA Bench 上展现出了比 Phi-3-vision-128K-Instruct 更强的多语言的性能。
Evaluation results of multilingual LLaVA Bench
多语言LLaVA Bench评测结果