Towards Understanding Best Practices for Quantization of Vision-Language Models
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A study investigates the effectiveness of various quantization methods, including GPTQ and AWQ, on multimodal pipelines involving vision and language models. Results show that both ViT and LLM are crucial for performance, with lower-bit quantization of LLM maintaining high accuracy. This research offers insights for optimizing memory and latency in deploying multimodal language models. The code is available at https://github.com/gautomdas/mmq.
Research Sheds Light on Quantization Best Practices for Vision-Language Models
Recent studies highlight the critical role of quantization in optimizing vision-language models (VLMs). This research explores various quantization methods, including GPTQ and AWQ techniques, to determine their effectiveness in multimodal pipelines that integrate vision and language models.
Key Findings from the Study
The investigation reveals significant insights into how different quantization strategies influence model performance across tasks such as captioning, retrieval, and question answering. Key outcomes include:
- Both Vision Transformers (ViTs) and LLMs play crucial roles in overall model performance.
- Implementing lower-bit quantization for LLMs can maintain high accuracy while significantly reducing bits per weight (bpw).
These results suggest that careful selection of quantization techniques is essential for optimizing VLMs in practical applications. The full code and methodologies used in this study are accessible at GitHub.
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📰 Original Source: https://arxiv.org/abs/2601.15287v1
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