Instructions to use microsoft/Florence-2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/Florence-2-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="microsoft/Florence-2-base", trust_remote_code=True)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use microsoft/Florence-2-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/Florence-2-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Florence-2-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/microsoft/Florence-2-base

SGLang

How to use microsoft/Florence-2-base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "microsoft/Florence-2-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Florence-2-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "microsoft/Florence-2-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Florence-2-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use microsoft/Florence-2-base with Docker Model Runner:
```
docker model run hf.co/microsoft/Florence-2-base
```

leoxiaobin

gar231 commited on Aug 4, 2025

Commit

2f6bee8

verified ·

1 Parent(s): 1896e7c

Fix: resize_token_embeddings's Interface for transformers v4.49.0 (#26)

Browse files

- Fix: resize_token_embeddings's Interface for transformers v4.49.0 (9990a98b2725653cba66babb82fbd007ae958b95)

Co-authored-by: YEN-FU LIN <gar231@users.noreply.huggingface.co>

Files changed (1) hide show

modeling_florence2.py +4 -4

modeling_florence2.py CHANGED Viewed

@@ -2078,8 +2078,8 @@ class Florence2LanguageForConditionalGeneration(Florence2LanguagePreTrainedModel
     def get_decoder(self):
         return self.model.get_decoder()
-    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
-        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
         self._resize_final_logits_bias(new_embeddings.weight.shape[0])
         return new_embeddings
@@ -2587,8 +2587,8 @@ class Florence2ForConditionalGeneration(Florence2PreTrainedModel):
     def get_input_embeddings(self):
         return self.language_model.get_input_embeddings()
-    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
-        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
         # update vocab size
         self.config.text_config.vocab_size = model_embeds.num_embeddings
         self.config.vocab_size = model_embeds.num_embeddings

     def get_decoder(self):
         return self.model.get_decoder()
+    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, **kwargs) -> nn.Embedding:
+        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, **kwargs)
         self._resize_final_logits_bias(new_embeddings.weight.shape[0])
         return new_embeddings
     def get_input_embeddings(self):
         return self.language_model.get_input_embeddings()
+    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None, **kwargs) -> nn.Embedding:
+        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of, **kwargs)
         # update vocab size
         self.config.text_config.vocab_size = model_embeds.num_embeddings
         self.config.vocab_size = model_embeds.num_embeddings