[gemma3] support sequence classification task #39465
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zucchini-nlp
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Jul 17, 2025
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| @unittest.skip("Loading nested configs with overwritten `kwargs` isn't supported yet, FIXME @raushan.") | ||
| def test_load_with_mismatched_shapes(self): | ||
| pass | ||
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| @unittest.skip("Loading nested configs with overwritten `kwargs` isn't supported yet, FIXME @raushan.") | ||
| def test_mismatched_shapes_have_properly_initialized_weights(self): | ||
| pass | ||
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Here I mean loading like Gemma3Config.from_dict(config_dict, vocab_size=100) where the vocab_size is actually part of config.text_config. It is a known issue and I have it in my plans to support
myvaheed
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Jul 17, 2025
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[For maintainers] Suggested jobs to run (before merge) run-slow: auto, gemma3 |
Cyrilvallez
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Jul 19, 2025
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Alright, but don't we have any other vlm from which we can inherit directly with modular?
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Nope, we usually don't add when there's no official pretrained checkpoint |
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* add seq clf class * fix docs and add in auto-map * skip tests * optional pixels
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* add seq clf class * fix docs and add in auto-map * skip tests * optional pixels
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* add seq clf class * fix docs and add in auto-map * skip tests * optional pixels
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* add seq clf class * fix docs and add in auto-map * skip tests * optional pixels
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* add seq clf class * fix docs and add in auto-map * skip tests * optional pixels
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What does this PR do?
As per title, we can't copy from llama or any other LLM because Gemma3 needs to obtain
text_configparams and needs to pass extra vision kwargs inforward. Thus the code was adapted from llama and the tests are greenFixes #36755