FlexiCT 3D VLM

--- license: cc-by-nc-nd-4.0 library_name: transformers pipeline_tag: zero-shot-image-classification tags: - medical-imaging - ct - vision-language --- # FlexiCT-3D-VLM FlexiCT-3D-VLM aligns the FlexiCT 3D vision encoder with a Qwen3 embedding text tower. ## Input and preprocessing Image preprocessing matches `FlexiCT-3D`: the default output shape is `[B, 1, 160, 160, 160]`. Text uses Qwen3 tokenizer behavior with left padding and max length `8192`. ```python from transformers import AutoModel, AutoProcessor processor = AutoProcessor.from_pretrained("ricklisz123/FlexiCT-3D-VLM", trust_remote_code=True) model = AutoModel.from_pretrained("ricklisz123/FlexiCT-3D-VLM", trust_remote_code=True) inputs = processor( images="/path/to/ct.nii.gz", text=["pulmonary nodule", "no acute abnormality"], return_tensors="pt", ) outputs = model(**inputs) similarity = outputs.logits_per_image ``` ## Outputs `image_embeds` and `text_embeds` are L2-normalized embeddings. `logits_per_image` contains learned-temperature-scaled image-text similarity scores. ## Limitations This model is for research retrieval and text-image scoring. It is not a diagnostic device. The text tower depends on Qwen3 tokenizer/config files unless they are already cached or included in the local repo.

Source context: 12 downloads · 0 likes · Pipeline zero-shot-image-classification · Library transformers · Repo ricklisz123/FlexiCT-3D-VLM