update

docs/source/en/model_doc/florence2.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -18,50 +18,51 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The Florence2 model was proposed in [<INSERT PAPER NAME HERE>](<INSERT PAPER LINK HERE>) by <INSERT AUTHORS HERE>.
-<INSERT SHORT SUMMARY HERE>
+The Florence2 model was proposed in [Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks](https://arxiv.org/abs/2311.06242) by Microsoft.
+
+Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model.
 
 The abstract from the paper is the following:
 
-*<INSERT PAPER ABSTRACT HERE>*
-
-Tips:
-
-<INSERT TIPS ABOUT MODEL HERE>
-
-This model was contributed by [INSERT YOUR HF USERNAME HERE](https://huggingface.co/<INSERT YOUR HF USERNAME HERE>).
-The original code can be found [here](<INSERT LINK TO GITHUB REPO HERE>).
+*We introduce Florence-2, a novel vision foundation model with a unified, prompt-based representation for a variety of computer vision and vision-language tasks. While existing large vision models excel in transfer learning, they struggle to perform a diversity of tasks with simple instructions, a capability that implies handling the complexity of various spatial hierarchy and semantic granularity. Florence-2 was designed to take text-prompt as task instructions and generate desirable results in text forms, whether it be captioning, object detection, grounding or segmentation. This multi-task learning setup demands large-scale, high-quality annotated data. To this end, we co-developed FLD-5B that consists of 5.4 billion comprehensive visual annotations on 126 million images, using an iterative strategy of automated image annotation and model refinement. We adopted a sequence-to-sequence structure to train Florence-2 to perform versatile and comprehensive vision tasks. Extensive evaluations on numerous tasks demonstrated Florence-2 to be a strong vision foundation model contender with unprecedented zero-shot and fine-tuning capabilities.*
 
+This model was contributed by [hlky](https://huggingface.co/hlky).
+The original code can be found [here](https://huggingface.co/microsoft/Florence-2-base/tree/main).
 
 ## Florence2Config
 
 [[autodoc]] Florence2Config
-    - all
 
-## Florence2Model
+## Florence2Processor
 
-[[autodoc]] Florence2Model
-    - forward
+[[autodoc]] Florence2Processor
 
 ## Florence2ForConditionalGeneration
 
 [[autodoc]] Florence2ForConditionalGeneration
     - forward
 
-## Florence2ForSequenceClassification
+## Florence2LanguageForConditionalGeneration
 
-[[autodoc]] Florence2ForSequenceClassification
+[[autodoc]] Florence2LanguageForConditionalGeneration
     - forward
 
-## Florence2ForQuestionAnswering
+## Florence2LanguageModel
 
-[[autodoc]] Florence2ForQuestionAnswering
+[[autodoc]] Florence2LanguageModel
     - forward
 
-## Florence2ForCausalLM
+## Florence2Vision
 
-[[autodoc]] Florence2ForCausalLM
+[[autodoc]] Florence2Vision
     - forward
 
-</pt>
-<tf>
+## Florence2VisionModel
+
+[[autodoc]] Florence2VisionModel
+    - forward
+
+## Florence2VisionModelWithProjection
+
+[[autodoc]] Florence2VisionModelWithProjection
+    - forward

src/transformers/models/auto/modeling_auto.py

@@ -119,6 +119,7 @@ MODEL_MAPPING_NAMES = OrderedDict(
         ("fastspeech2_conformer", "FastSpeech2ConformerModel"),
         ("flaubert", "FlaubertModel"),
         ("flava", "FlavaModel"),
+        ("florence2", "Florence2ForConditionalGeneration"),
         ("fnet", "FNetModel"),
         ("focalnet", "FocalNetModel"),
         ("fsmt", "FSMTModel"),
@@ -884,8 +885,8 @@ MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES = OrderedDict(
         ("blip-2", "Blip2ForConditionalGeneration"),
         ("chameleon", "ChameleonForConditionalGeneration"),
         ("emu3", "Emu3ForConditionalGeneration"),
-        ("fuyu", "FuyuForCausalLM"),
         ("florence2", "Florence2ForConditionalGeneration"),
+        ("fuyu", "FuyuForCausalLM"),
         ("gemma3", "Gemma3ForConditionalGeneration"),
         ("git", "GitForCausalLM"),
         ("got_ocr2", "GotOcr2ForConditionalGeneration"),

src/transformers/models/florence2/configuration_florence2.py

@@ -322,3 +322,6 @@ class Florence2Config(PretrainedConfig):
             self.text_config = Florence2LanguageConfig(**text_config)
 
         super().__init__(**kwargs)
+
+
+__all__ = ["Florence2Config"]

src/transformers/models/florence2/modular_florence2.py

@@ -28,7 +28,7 @@ from ...utils import (  # noqa: F401
     logging,
     replace_return_docstrings,
 )
-from ..bart.modeling_bart import BartForConditionalGeneration
+from ..bart.modeling_bart import BartForConditionalGeneration, BartPreTrainedModel
 from .configuration_florence2 import Florence2Config, Florence2LanguageConfig, Florence2VisionConfig  # noqa: F401
 
 
@@ -984,6 +984,14 @@ class Florence2VisionModelWithProjection(Florence2PreTrainedModel):
         return x
 
 
+class Florence2LanguagePreTrainedModel(BartPreTrainedModel):
+    pass
+
+
+class Florence2LanguageModel(Florence2LanguagePreTrainedModel):
+    pass
+
+
 class Florence2LanguageForConditionalGeneration(BartForConditionalGeneration):
     pass
 
@@ -1296,3 +1304,15 @@ class Florence2ForConditionalGeneration(Florence2PreTrainedModel):
 
     def _reorder_cache(self, *args, **kwargs):
         return self.language_model._reorder_cache(*args, **kwargs)
+
+
+__all__ = [
+    "Florence2ForConditionalGeneration",
+    "Florence2LanguageForConditionalGeneration",
+    "Florence2LanguageModel",
+    "Florence2LanguagePreTrainedModel",
+    "Florence2PreTrainedModel",
+    "Florence2Vision",
+    "Florence2VisionModel",
+    "Florence2VisionModelWithProjection",
+]

src/transformers/models/florence2/processing_florence2.py

@@ -159,7 +159,7 @@ class Florence2Processor(ProcessorMixin):
             "<REGION_TO_OCR>": "What text is in the region {input}?",
         }
 
-        self.post_processor = Florence2PostProcesser(tokenizer=tokenizer)
+        self.post_processor = Florence2PostProcessor(tokenizer=tokenizer)
 
         super().__init__(image_processor, tokenizer)
 
@@ -513,7 +513,7 @@ class CoordinatesQuantizer(object):
         return dequantized_coordinates
 
 
-class Florence2PostProcesser(object):
+class Florence2PostProcessor(object):
     r"""
     Florence-2 post process for converting text prediction to various tasks results.
 
@@ -1220,3 +1220,6 @@ class Florence2PostProcesser(object):
                 raise ValueError("task {} is not supported".format(task))
 
         return parsed_dict
+
+
+__all__ = ["Florence2Processor"]

tests/models/florence2/test_processor_florence2.py

@@ -0,0 +1,63 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import shutil
+import tempfile
+import unittest
+
+from transformers import AutoProcessor, AutoTokenizer, BartTokenizerFast, Florence2Processor
+from transformers.testing_utils import require_vision
+from transformers.utils import is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_vision_available():
+    from transformers import CLIPImageProcessor
+
+
+@require_vision
+class Florence2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Florence2Processor
+
+    def setUp(self):
+        self.tmpdirname = tempfile.mkdtemp()
+
+        image_processor = CLIPImageProcessor.from_pretrained("microsoft/Florence-2-base")
+        tokenizer = BartTokenizerFast.from_pretrained("microsoft/Florence-2-base")
+        processor_kwargs = self.prepare_processor_dict()
+        processor = Florence2Processor(image_processor, tokenizer, **processor_kwargs)
+        processor.save_pretrained(self.tmpdirname)
+
+    def get_tokenizer(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
+
+    def get_image_processor(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
+
+    def tearDown(self):
+        shutil.rmtree(self.tmpdirname)
+
+    @unittest.skip(
+        "Skip because the model has no processor kwargs except for chat template and"
+        "chat template is saved as a separate file. Stop skipping this test when the processor"
+        "has new kwargs saved in config file."
+    )
+    def test_processor_to_json_string(self):
+        pass
+
+    def test_can_load_various_tokenizers(self):
+        for checkpoint in ["microsoft/Florence-2-base"]:
+            processor = Florence2Processor.from_pretrained(checkpoint)
+            tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+            self.assertEqual(processor.tokenizer.__class__, tokenizer.__class__)

utils/check_repo.py

@@ -164,6 +164,11 @@ IGNORE_NON_TESTED = (
         "CsmDepthDecoderForCausalLM",  # Building part of bigger (tested) model. Tested implicitly through CsmForConditionalGenerationIntegrationTest.
         "CsmDepthDecoderModel",  # Building part of bigger (tested) model. Tested implicitly through CsmForConditionalGenerationIntegrationTest.
         "CsmBackboneModel",  # Building part of bigger (tested) model. Tested implicitly through CsmForConditionalGenerationIntegrationTest.
+        "Florence2LanguageModel",  # Building part of bigger (tested) model. Tested implicitly through Florence2ForConditionalGeneration.
+        "Florence2Vision",  # Building part of bigger (tested) model. Tested implicitly through Florence2ForConditionalGeneration.
+        "Florence2VisionModel",  # Building part of bigger (tested) model. Tested implicitly through Florence2ForConditionalGeneration.
+        "Florence2VisionModelWithProjection",  # Building part of bigger (tested) model. Tested implicitly through Florence2ForConditionalGeneration.
+        "Florence2LanguageForConditionalGeneration",  # Building part of bigger (tested) model. Tested implicitly through Florence2ForConditionalGeneration.
     ]
 )
 
@@ -377,6 +382,11 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
     "CsmDepthDecoderModel",  # Building part of a bigger model
     "CsmDepthDecoderForCausalLM",  # Building part of a bigger model
     "CsmForConditionalGeneration",  # Building part of a bigger model
+    "Florence2LanguageForConditionalGeneration",  # Building part of a bigger model
+    "Florence2LanguageModel",  # Building part of a bigger model
+    "Florence2Vision",  # Building part of a bigger model
+    "Florence2VisionModel",  # Building part of a bigger model
+    "Florence2VisionModelWithProjection",  # Building part of a bigger model
 ]
 
 # DO NOT edit this list!