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

Libraries

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

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

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

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/Florence-2-large"
# 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-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use microsoft/Florence-2-large 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-large" \
    --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-large",
		"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-large" \
        --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-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

modeling_florence2.py incompatible with transformers>=4.54.0: _supports_sdpa AttributeError

#115

by j2gg0s - opened Mar 17

Discussion

j2gg0s

Mar 17

Bug

Florence2ForConditionalGeneration fails to load with transformers==4.54.0:

model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large", trust_remote_code=True)
# AttributeError: 'Florence2ForConditionalGeneration' object has no attribute '_supports_sdpa'

Root Cause

In modeling_florence2.py, Florence2PreTrainedModel._supports_sdpa is a @property that delegates to
self.language_model._supports_sdpa. But transformers 4.54.0 checks self._supports_sdpa inside
PreTrainedModel.__init__(), before self.language_model is assigned in the subclass __init__.

Suggested Fix

Add hasattr guard to the property:

class Florence2PreTrainedModel(PreTrainedModel):
    @property
    def _supports_sdpa(self):
        if not hasattr(self, 'language_model'):
            return True
        return self.language_model._supports_sdpa

    @property
    def _supports_flash_attn_2(self):
        if not hasattr(self, 'language_model'):
            return True
        return self.language_model._supports_flash_attn_2

Environment

- transformers==4.54.0
- torch==2.x
- Python 3.10

userx1

Mar 25

bump. same issue here.

vthorey

Apr 8

The models were converted in this repo: https://huggingface.co/florence-community
I just switched and it works with latests versions of transformers

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