Fastvlm: Well-installed installation of the vision of the vision language

Measuring the installation image is essential to enhance the performance of vision models (VLMS), especially in the understanding of the understanding of the image. However, known visual familiarities such as vitit is not working properly in maximum decisions due to large number of tokens and high ACCODING ACCENCY. Unique app settlements, the Encoder Encoder Encoder may repair two axes: reducing the installation of the written latency and reduces the number of visible tokens to the LLM, thus reducing the full latency. Based on the total interactions between the decision of the image, the Vision Latency, token to the calculator, and the size of the llM, is a Suppevm – a model that reaches the prepared trading of the decision, latency, and accuracy. Fastvlm includes Fastvithds, the Encoder's novel designed to remove a few tokens and reduces the time to enter the photo details. Unlike previous ways, FastVLM reaches the appropriate balance between calculating visual and photo adjustments by measuring the installation image, the need for further purchase and simplify the additional formation of Token. In LAVA-1.5 SETUP, FASTVLM reaches 3.2x development in Time-to-Token (TTFT) while maintaining VLM benches compared to previous jobs. Compared with the high-optakes of higher resolution (1152×152), Fastvlm reaches compatible at the key benches such as 0.5B LLF, but at 85x TTFT and the 3.4x encoder.