Instructions to use HuggingFaceM4/Florence-2-DocVQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceM4/Florence-2-DocVQA with Transformers:

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

pipe = pipeline("image-text-to-text", model="HuggingFaceM4/Florence-2-DocVQA", trust_remote_code=True)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("HuggingFaceM4/Florence-2-DocVQA", trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained("HuggingFaceM4/Florence-2-DocVQA", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HuggingFaceM4/Florence-2-DocVQA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceM4/Florence-2-DocVQA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceM4/Florence-2-DocVQA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceM4/Florence-2-DocVQA

SGLang

How to use HuggingFaceM4/Florence-2-DocVQA 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 "HuggingFaceM4/Florence-2-DocVQA" \
    --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": "HuggingFaceM4/Florence-2-DocVQA",
		"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 "HuggingFaceM4/Florence-2-DocVQA" \
        --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": "HuggingFaceM4/Florence-2-DocVQA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceM4/Florence-2-DocVQA with Docker Model Runner:
```
docker model run hf.co/HuggingFaceM4/Florence-2-DocVQA
```

andito HF Staff commited on Jun 25, 2024

Commit

2b92763

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1 Parent(s): aa0881b

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@@ -7,6 +7,8 @@ tags: []
 <!-- Provide a quick summary of what the model is/does. -->
 This is Microsoft's Florence-2 model trained for 9 epochs on the training set from DocVQA with a learning rate of 1e-6.
 ## Model Details
@@ -15,7 +17,7 @@ This is Microsoft's Florence-2 model trained for 9 epochs on the training set fr
 <!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 - **Developed by:** Andi Marafioti
 - **Funded by [optional]:** Hugging Face 🤗

 <!-- Provide a quick summary of what the model is/does. -->
 This is Microsoft's Florence-2 model trained for 9 epochs on the training set from DocVQA with a learning rate of 1e-6.
+The code for this fine-tuning can be found here: https://github.com/andimarafioti/florence2-finetuning
+And here's a blog explaining how to fine tune Florence: https://huggingface.co/blog/finetune-florence2
 ## Model Details
 <!-- Provide a longer summary of what this model is. -->
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. It has been automatically generated.
 - **Developed by:** Andi Marafioti
 - **Funded by [optional]:** Hugging Face 🤗