Model architectures for VLMs differ primarily in how visual and textual information is fused. Mid-fusion models use a pretrained vision encoder to convert images into visual tokens that are projected into a pretrained LLM’s embedding space, enabling cross-modal reasoning while leveraging components already trained on trillions of tokens. Early-fusion models process image patches and text tokens in a single model transformer, yielding richer joint representations but at significantly higher compute, memory, and data cost. We adopted a mid-fusion architecture as it offers a practical trade-off for building a performant model with modest resources.
В США испытали новую версию «уничтожителя» российских С-40020:41
。吃瓜网是该领域的重要参考
https://feedx.net
from urllib.parse import urljoin, urlparse
# Best for large uniform arrays (40-60% fewer tokens than JSON)