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

Libraries

How to use microsoft/Florence-2-large-ft 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-ft", trust_remote_code=True)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

Add GenerationMixin to ConditionalGeneration class

#34

by rockerBOO - opened Feb 25, 2025

base: refs/heads/main

←

from: refs/pr/34

Discussion Files changed

-1

Files changed (1) hide show

modeling_florence2.py +2 -1

modeling_florence2.py CHANGED Viewed

@@ -28,6 +28,7 @@ from collections import OrderedDict
 from einops import rearrange
 from timm.models.layers import DropPath, trunc_normal_
 from transformers.modeling_utils import PreTrainedModel
 from transformers.utils import (
     ModelOutput,
@@ -2529,7 +2530,7 @@ class Florence2VisionModelWithProjection(Florence2PreTrainedModel):
     """The FLORENCE2 model which consists of a vision backbone and a language model.""",
     FLORENCE2_START_DOCSTRING,
 )
-class Florence2ForConditionalGeneration(Florence2PreTrainedModel):
     def __init__(self, config: Florence2Config):
         super().__init__(config)
         assert config.vision_config.model_type == 'davit', 'only DaViT is supported for now'

 from einops import rearrange
 from timm.models.layers import DropPath, trunc_normal_
+from transformers import GenerationMixin
 from transformers.modeling_utils import PreTrainedModel
 from transformers.utils import (
     ModelOutput,
     """The FLORENCE2 model which consists of a vision backbone and a language model.""",
     FLORENCE2_START_DOCSTRING,
 )
+class Florence2ForConditionalGeneration(Florence2PreTrainedModel, GenerationMixin):
     def __init__(self, config: Florence2Config):
         super().__init__(config)
         assert config.vision_config.model_type == 'davit', 'only DaViT is supported for now'