Fal-2: Efficient Vision-Language Model

Fal-2 is a compact vision-language model optimized for fast, high-quality image understanding. It uses a hybrid vision encoder that emits far fewer tokens for high-resolution images and a lightweight causal decoding head for fluent descriptions.

Highlights

Model Size: 500M parameters (small and efficient)

Efficient Token Generation: 256 tokens at 1024×1024 (≈16× fewer tokens vs typical ViT encoders)

Primary Use: Image captioning, visual question answering, and lightweight multimodal assistants