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
```

Clarification on JSON Lines Dataset for Multi-Task Fine-Tuning of Florence-2

#78

by mariaac - opened Oct 14, 2024

Discussion

mariaac

Oct 14, 2024

Hi everyone,

I came across the notebook discussing how to fine-tune Florence-2 for Object Detection, and I have a question regarding the structure of the JSON Lines dataset when fine-tuning for multiple tasks.

Specifically, how should the dataset be formatted if I want to fine-tune for more than one task?

Should the prefix field be a list of task string IDs, while the suffix field contains a list of strings that represent the answers for each task? For example, would the following structure be correct?

{
  "prefix": ["<OD>", "<OCR>"],
  "suffix": [
    "ace of hearts<loc_345><loc_315><loc_582><loc_721>2 of hearts<loc_709><loc_115><loc_888><loc_509>3 of hearts<loc_529><loc_228><loc_735><loc_613>4 of hearts<loc_98><loc_421><loc_415><loc_845>",
    "answer_for_ocr"
  ]
}

Additionally, is there a guide available on how to format datasets for each task?

I appreciate any guidance on this!

Thank you!

mariaac

Oct 14, 2024

Hi @merve :) Could you help me out with this?

Andyrasika

Oct 15, 2024

@mariaac looking into it

mariaac

Oct 17, 2024

Hi @Andyrasika ,

What about finetuning for OCR with region?
Should the dataset be formatted in a similar way to the OD dataset?

{"image": "path/to/image.png", "prefix": "<OCR_WITH_REGION>", "suffix": "US<loc_895><loc_505><loc_965><loc_555>Hugging Face<loc_647><loc_164><loc_747><loc_216>"}

I haven't found any example for finetuning for this task

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