Visual Encoder

Multimodal large language models align visual encoders (such as CLIP and ViT) with language models through contrastive learning.

A VLM (vision-language model) is a type of model that simultaneously accepts image and text inputs and performs reasoning in a shared representation space.

Using the space freed up by speech quantization, the developer raised the context from 32K to 100K and added MTP (for accelerated tool-call prediction) and a vision encoder.

Evaluation of vision encoders should not stop at accuracy metrics within the natural image distribution; semantic consistency in off-manifold regions is equally critical.

The vision encoders of multimodal large models are usually frozen or only lightly adapted, limiting their domain adaptation capability.

Current mainstream multimodal architectures can be summarized as the 'vision encoder + language model' concatenation paradigm, whose implicit assumption is that language is the vehicle of general reasoning.

The vision encoder is typically composed of a convolutional neural network (CNN) or a vision Transformer (ViT), which cuts the image into several patches or regions and extracts high-dimensional feature vectors patch by patch.

Multimodal large language models (MLLMs) are large-scale neural network models capable of simultaneously processing and understanding multiple modalities such as text, images, audio, and video, typically based on the Transformer architecture, and acquiring cross-modal alignment capabilities through pretraining on large-scale multimodal data.

Integrating the two visual views into the same language model requires injecting them into a shared language model backbone through visual tokens and multi-level feature fusion, so as to avoid losing their respective characteristics through simple early concatenation.

The language model uses the W8A8 quantized version in RKLLM format; the vision encoder uses the RKNN format.

Deployment troubleshooting suggestions: when debugging, if text Q&A works normally but image Q&A fails, the problem is most likely in the vision encoder chain rather than the language model itself; when startup reports an error, first locate which step it occurs at (library not found or model loading failure) before troubleshooting.

This deployment uses two model files: the language model is responsible for generating answers, and the vision encoder is responsible for processing images; pure text questions go only through the language model, while when an image is uploaded the image first passes through the vision encoder to extract features and is then handed to the language model together with the question.

When Claude receives a screenshot, the image is first split into patches by the vision encoder, each patch is converted into visual tokens, and these are then concatenated with text tokens into the same context window.

The visual encoder has an extremely small parameter count (typically a few hundred MB), requiring no retraining of the main model — image understanding capability can be activated simply by loading additional weights.

NVIDIA's proposed Encode-Prefill-Decode (EPD) disaggregation technology improves multimodal model serving efficiency by decoupling the visual encoder from the prefill and decode stages and deploying and scheduling it independently.

The real differences between visual encoders lie not in model architecture, but in training objectives.

Today's mainstream visual encoders are highly similar at the underlying architecture level, with almost all being based on Transformers.