Inverse-LLaVA maps language features into the visual feature space and fuses them inside the language decoder. This is the full-data 7B checkpoint trained jointly on visual instructions, without a separate alignment-pretraining stage. On Linux with a CUDA-compatible PyTorch installation, install the source package: Use a commit hash in revision= to pin a particular Hub snapshot. The weights file has the stable identity below; the loader verifies its checksums before constructing the model. It downloads the pinned Vicuna and CLIP weights separately when they are not cached. The 695 MB delta is not the total download or GPU-memory requirement. Allow space for the approximately 14 GB language…