FlexiCT 3D VLM

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.

--- 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.