A full fine-tune of OpenGVLab/InternVL35-2B-HF (vision tower, projector and LLM all trained) on rejection-sampled, verified answers from public biomedical datasets. No MMBU data was used, and every training image passes a pHash near-duplicate check against the MMBU public images. - RFT data. The base model answered public classification, fine-grained, detection and counting questions 6 times each. Only answers that verified against the ground truth were kept. Hint-rationalized answers were added for unsolved questions, and box-answer rows were excluded. - Metadata supervision (about 30% of the mix). Targets are modality, submodality, specimen, body part, stain and medical domain. Medical…
Independent publisher
Nitiz Khanal
StarAtNyte1
Models in Library1
Datasets in Library0
Models on Hugging Face7
Followers2
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Published tasks: Image and text to text.
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