4.0 KiB
Phi-3-vision-128K-Instruct vs MiniCPM-Llama3-V 2.5
Comparison results of Phi-3-vision-128K-Instruct and MiniCPM-Llama3-V 2.5, regarding the model size, hardware requirements, and performances.
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。
Hardeware Requirements （硬件需求）
With int4 quantization, MiniCPM-Llama3-V 2.5 delivers smooth inference with only 8GB of GPU memory.
通过 int4 量化，MiniCPM-Llama3-V 2.5 仅需 8GB 显存即可推理。
| Model（模型） | GPU Memory（显存） |
|---|---|
| MiniCPM-Llama3-V 2.5 | 19 GB |
| Phi-3-vision-128K-Instruct | 12 GB |
| MiniCPM-Llama3-V 2.5 (int4) | 8 GB |
Model Size and Peformance （模型参数和性能）
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.
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。
| Phi-3-vision-128K-Instruct | MiniCPM-Llama3-V 2.5 | |
|---|---|---|
| Size（参数） | 4B | 8B |
| First Token Latency（首token延迟）2 | L: 330ms, M: 330ms, H: 330ms | L: 48ms, M: 145ms, H: 278ms |
| Throughtput（吞吐率）2 | 30 tokens/s | 41 tokens/s |
| OpenCompass 2024/05 | 53.7 | 58.8 |
| OCRBench | 639.0 | 725.0 |
| RealworldQA | 58.8 | 63.5 |
| TextVQA | 72.2 | 76.6 |
| ScienceQA | 90.8 | 89.0 |
| POPE | 83.4 | 87.2 |
| MathVista | 44.5 | 54.3 |
| MMStar | 47.4 | 51.8 |
| LLaVA Bench | 64.2 | 86.7 |
| AI2D | 76.7 | 78.4 |
1. Evaluation environment: A800 GPU, flash-attn=2.4.3, batch-size=1.
MiniCPM-Llama3-V 2.5 shows better first token latency and throughput performance even though the number of parameters is twice as large as that of Phi-3-vision-128k-instruct due to its efficient image encoding method and adaptive resolution encoding strategy. For example, for an input images with a 448x448 resolution, MiniCPM-Llama3-V 2.5 encodes it into 96 tokens, while Phi-3-vision-128k-instruct encodes it into 2500+ tokens. Longer image token sequence significantly affects the first token latency and throughput. MiniCPM-V series models insist on obtaining stronger performance with more efficient encoding, thus achieves efficient end-device deployment and providing better experience for end users.
得益于 MiniCPM-Llama3-V 2.5 高效的图像编码方式和自适应分辨率编码策略，即使参数量比 Phi-3-vision-128k-instruct 大一倍，依然展现出了更出色的首 token 延迟和吞吐量表现。例如两个模型对输入分辨率为448x448 的图像，MiniCPM-Llama3-V 2.5 的图像编码长度为 96， 而Phi-3-vision-128k-instruct 的图像编码长度为 2500+。更长的图像编码长度会显著影响首token延迟和吞吐量，MiniCPM-V系列坚持用更高效的编码方式撬动更强的性能，进而实现高效的终端设备部署，为端侧用户提供更良好的体验。
Multilingual Capabilities
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 LLaVABench in multiple languages
多语言LLaVA Bench评测结果