Fix ckpt in docs (#41659)

* fix ckpt in docs

* fix config ckpt

Author: zucchini-nlp

docs/source/en/model_doc/florence2.md

@@ -70,8 +70,8 @@ from transformers import AutoProcessor, Florence2ForConditionalGeneration
 url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
 image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
 
-model = Florence2ForConditionalGeneration.from_pretrained("microsoft/Florence-2-base", dtype=torch.bfloat16, device_map="auto")
-processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base")
+model = Florence2ForConditionalGeneration.from_pretrained("florence-community/Florence-2-base", dtype=torch.bfloat16, device_map="auto")
+processor = AutoProcessor.from_pretrained("florence-community/Florence-2-base")
 
 task_prompt = "<OD>"
 inputs = processor(text=task_prompt, images=image, return_tensors="pt").to(model.device)
@@ -105,12 +105,12 @@ from transformers import AutoProcessor, Florence2ForConditionalGeneration, BitsA
 quantization_config = BitsAndBytesConfig(load_in_4bit=True)
 
 model = Florence2ForConditionalGeneration.from_pretrained(
-    "microsoft/Florence-2-large",
+    "florence-community/Florence-2-base",
     dtype=torch.bfloat16,
     device_map="auto",
     quantization_config=quantization_config
 )
-processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large")
+processor = AutoProcessor.from_pretrained("florence-community/Florence-2-base")
 
 url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
 image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

src/transformers/models/florence2/configuration_florence2.py

@@ -140,7 +140,7 @@ class Florence2Config(PreTrainedConfig):
     Florence-2 model according to the specified arguments, defining the model architecture.
 
     Instantiating a configuration with the defaults will yield a similar configuration to that of the Florence-2
-    [microsoft/Florence-2-base](https://huggingface.co/microsoft/Florence-2-base) architecture.
+    [florence-community/Florence-2-base](https://huggingface.co/florence-community/Florence-2-base) architecture.
 
     Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
     documentation from [`PreTrainedConfig`] for more information.

src/transformers/models/florence2/modeling_florence2.py

@@ -884,8 +884,8 @@ def forward(
         >>> import requests
         >>> from transformers import AutoProcessor, Florence2ForConditionalGeneration
 
-        >>> model = Florence2ForConditionalGeneration.from_pretrained("microsoft/Florence-2-large")
-        >>> processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large")
+        >>> model = Florence2ForConditionalGeneration.from_pretrained("florence-community/Florence-2-large")
+        >>> processor = AutoProcessor.from_pretrained("florence-community/Florence-2-large")
 
         >>> prompt = "<CAPTION>"
         >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"

src/transformers/models/florence2/modular_florence2.py

@@ -160,7 +160,7 @@ class Florence2Config(PreTrainedConfig):
     Florence-2 model according to the specified arguments, defining the model architecture.
 
     Instantiating a configuration with the defaults will yield a similar configuration to that of the Florence-2
-    [microsoft/Florence-2-base](https://huggingface.co/microsoft/Florence-2-base) architecture.
+    [florence-community/Florence-2-base](https://huggingface.co/florence-community/Florence-2-base) architecture.
 
     Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
     documentation from [`PreTrainedConfig`] for more information.
@@ -1674,8 +1674,8 @@ def forward(
         >>> import requests
         >>> from transformers import AutoProcessor, Florence2ForConditionalGeneration
 
-        >>> model = Florence2ForConditionalGeneration.from_pretrained("microsoft/Florence-2-large")
-        >>> processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large")
+        >>> model = Florence2ForConditionalGeneration.from_pretrained("florence-community/Florence-2-large")
+        >>> processor = AutoProcessor.from_pretrained("florence-community/Florence-2-large")
 
         >>> prompt = "<CAPTION>"
         >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"