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 commited on Jun 18, 2024

Commit

a2f0be7

verified ·

1 Parent(s): 0f6358c

Update README.md

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README.md +21 -19

README.md CHANGED Viewed

@@ -110,20 +110,7 @@ Here are the tasks `Florence-2` could perform:
 <details>
 <summary> Click to expand </summary>
-### OCR
-```python
-prompt = "<OCR>"
-run_example(prompt)
-```
-### OCR with Region
-OCR with region output format:
-{'\<OCR_WITH_REGION>': {'quad_boxes': [[x1, y1, x2, y2, x3, y3, x4, y4], ...], 'labels': ['text1', ...]}}
-```python
-prompt = "<OCR_WITH_REGION>"
-run_example(prompt)
-```
 ### Caption
 ```python
@@ -143,6 +130,16 @@ prompt = "<MORE_DETAILED_CAPTION>"
 run_example(prompt)
 ```
 ### Object Detection
 OD results format:
@@ -172,14 +169,19 @@ prompt = "<REGION_PROPOSAL>"
 run_example(prompt)
 ```
-### Caption to Phrase Grounding
-caption to phrase grounding task requires additional text input, i.e. caption.
-Caption to phrase grounding results format:
-{'\<CAPTION_TO_PHRASE_GROUNDING>': {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['', '', ...]}}
 ```python
-task_prompt = "<CAPTION_TO_PHRASE_GROUNDING>"
-results = run_example(task_prompt, text_input="A green car parked in front of a yellow building.")
 ```
 for More detailed examples, please refer to [notebook](https://huggingface.co/microsoft/Florence-2-large/blob/main/sample_inference.ipynb)

 <details>
 <summary> Click to expand </summary>
 ### Caption
 ```python
 run_example(prompt)
 ```
+### Caption to Phrase Grounding
+caption to phrase grounding task requires additional text input, i.e. caption.
+Caption to phrase grounding results format:
+{'\<CAPTION_TO_PHRASE_GROUNDING>': {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['', '', ...]}}
+```python
+task_prompt = "<CAPTION_TO_PHRASE_GROUNDING>"
+results = run_example(task_prompt, text_input="A green car parked in front of a yellow building.")
+```
 ### Object Detection
 OD results format:
 run_example(prompt)
 ```
+### OCR
 ```python
+prompt = "<OCR>"
+run_example(prompt)
+```
+### OCR with Region
+OCR with region output format:
+{'\<OCR_WITH_REGION>': {'quad_boxes': [[x1, y1, x2, y2, x3, y3, x4, y4], ...], 'labels': ['text1', ...]}}
+```python
+prompt = "<OCR_WITH_REGION>"
+run_example(prompt)
 ```
 for More detailed examples, please refer to [notebook](https://huggingface.co/microsoft/Florence-2-large/blob/main/sample_inference.ipynb)