mtmd: dsocr-tiles test fixes - #25486
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ngxson merged 1 commit intoJul 10, 2026
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…pt space; make DRY opt-in and n_predict model-specific
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The reg-test gate/tolerance is relaxed because testing with the HF ref on CPU and CUDA (and a modified HF ref on Metal/mps) showed the baseline should be set higher, and the tolerance should be wider when considering different weight dtypes and backends. This is all verified and reproducible: https://github.com/sfallah/llama-mtmd-eval |
ngxson
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* mtmd: deepseek-ocr v1 multi-tile dynamic resolution + unified image-preprocessors for both versions (ds-ocr v1 and v2) * remove hacky API * fuse row into a long image * almost working * adapt to new preprocessor api * rm debugging printf * improve * mtmd: dsocr-tiles fixes (#25481) * ds-ocr img-preproc fuse_row tile-drop fix for multi rows and columns images * mtmd drop the duplicate redundant img_end * deepseekocr graph simplify CLS broadcast cleanup * test-deepseek-ocr: relax v1 single-view tolerance; drop trailing prompt space; make DRY opt-in and n_predict model-specific (#25486) --------- Co-authored-by: Saba Fallah <10401143+sfallah@users.noreply.github.com> Co-authored-by: Saba Fallah <sabafallah@gmail.com>
satindergrewal
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Aug 12, 2026
* mtmd: deepseek-ocr v1 multi-tile dynamic resolution + unified image-preprocessors for both versions (ds-ocr v1 and v2) * remove hacky API * fuse row into a long image * almost working * adapt to new preprocessor api * rm debugging printf * improve * mtmd: dsocr-tiles fixes (ggml-org#25481) * ds-ocr img-preproc fuse_row tile-drop fix for multi rows and columns images * mtmd drop the duplicate redundant img_end * deepseekocr graph simplify CLS broadcast cleanup * test-deepseek-ocr: relax v1 single-view tolerance; drop trailing prompt space; make DRY opt-in and n_predict model-specific (ggml-org#25486) --------- Co-authored-by: Saba Fallah <10401143+sfallah@users.noreply.github.com> Co-authored-by: Saba Fallah <sabafallah@gmail.com>
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AI usage disclosure: YES - I used AI assistance for code review, debugging, implementation checks, and testing. I have reviewed the submitted changes and take responsibility for the full contents of this PR.