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, AutoModelForImageTextToText

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

Notebooks
Google Colab
Kaggle
Local Apps

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
```

Regarding Confidence Score

#36

by Haneesh-007 - opened Jul 1, 2024

Discussion

Haneesh-007

Jul 1, 2024

How do I get confidence score for bounding boxes while inferencing Object Detection task in FLorence-2?

chenxiangyi10

Feb 18, 2025

I would like to know how the scores are calculated when setting "output_scores=True", please provide a reference for the score's meaning.

JENNSHIUAN

Mar 4, 2025

•

edited Mar 4, 2025

def run_example_with_score(task_prompt, text_input=None):
    if text_input is None:
        prompt = task_prompt
    else:
        prompt = task_prompt + text_input
    inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype)
    generated_ids = model.generate(
      input_ids=inputs["input_ids"],
      pixel_values=inputs["pixel_values"],
      max_new_tokens=1024,
      num_beams=3,
      return_dict_in_generate=True,
      output_scores=True,
    )
    generated_text = processor.batch_decode(generated_ids.sequences, skip_special_tokens=False)[0]
    prediction, scores, beam_indices = generated_ids.sequences, generated_ids.scores, generated_ids.beam_indices
    transition_beam_scores = model.compute_transition_scores(
        sequences=prediction,
        scores=scores,
        beam_indices=beam_indices,
    )
    parsed_answer = processor.post_process_generation(sequence=generated_ids.sequences[0], 
        transition_beam_score=transition_beam_scores[0],
        task=task_prompt, image_size=(image.width, image.height)
    )

    print(parsed_answer)

here is the resource: https://huggingface.co/microsoft/Florence-2-large/discussions/56/files

ocg2347

Feb 27

any idea what version of transformers works for this? I tried a lot but got

Florence2Processor.post_process_generation() got an unexpected keyword argument 'transition_beam_score'

Florence2Processor.post_process_generation() got an unexpected keyword argument 'sequence'

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