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

Libraries

How to use ucsahin/Florence-2-large-TableDetection with Transformers:

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

pipe = pipeline("image-text-to-text", model="ucsahin/Florence-2-large-TableDetection", trust_remote_code=True)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ucsahin/Florence-2-large-TableDetection with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ucsahin/Florence-2-large-TableDetection

SGLang

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

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

ucsahin commited on Jun 25, 2024

Commit

93306a5

verified ·

1 Parent(s): e394b9c

Upload 2 files

Browse files

Files changed (2) hide show

preprocessor_config.json +1 -1
processor_config.json +6 -0

preprocessor_config.json CHANGED Viewed

@@ -17,7 +17,7 @@
     "input_data_format"
   ],
   "auto_map": {
-    "AutoProcessor": "microsoft/Florence-2-large-ft--processing_florence2.Florence2Processor"
   },
   "crop_size": {
     "height": 768,

     "input_data_format"
   ],
   "auto_map": {
+    "AutoProcessor": "processing_florence2.Florence2Processor"
   },
   "crop_size": {
     "height": 768,

processor_config.json ADDED Viewed

	@@ -0,0 +1,6 @@

+{
+  "auto_map": {
+    "AutoProcessor": "processing_florence2.Florence2Processor"
+  },
+  "processor_class": "Florence2Processor"
+}