Instructions to use Xenova/tiny-random-Florence2ForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Xenova/tiny-random-Florence2ForConditionalGeneration with Transformers:

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

pipe = pipeline("image-text-to-text", model="Xenova/tiny-random-Florence2ForConditionalGeneration", trust_remote_code=True)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Xenova/tiny-random-Florence2ForConditionalGeneration", trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained("Xenova/tiny-random-Florence2ForConditionalGeneration", trust_remote_code=True)

Transformers.js

How to use Xenova/tiny-random-Florence2ForConditionalGeneration with Transformers.js:

// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';

// Allocate pipeline
const pipe = await pipeline('image-text-to-text', 'Xenova/tiny-random-Florence2ForConditionalGeneration');

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Xenova/tiny-random-Florence2ForConditionalGeneration with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Xenova/tiny-random-Florence2ForConditionalGeneration"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Xenova/tiny-random-Florence2ForConditionalGeneration",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Xenova/tiny-random-Florence2ForConditionalGeneration

SGLang

How to use Xenova/tiny-random-Florence2ForConditionalGeneration 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 "Xenova/tiny-random-Florence2ForConditionalGeneration" \
    --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": "Xenova/tiny-random-Florence2ForConditionalGeneration",
		"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 "Xenova/tiny-random-Florence2ForConditionalGeneration" \
        --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": "Xenova/tiny-random-Florence2ForConditionalGeneration",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Xenova/tiny-random-Florence2ForConditionalGeneration with Docker Model Runner:
```
docker model run hf.co/Xenova/tiny-random-Florence2ForConditionalGeneration
```

Xenova HF Staff commited on Jun 20, 2024

Commit

440cbcc

verified ·

1 Parent(s): d9f6469

Update config.json

Browse files

Files changed (1) hide show

config.json +4 -0

config.json CHANGED Viewed

@@ -2,6 +2,10 @@
   "architectures": [
     "Florence2ForConditionalGeneration"
   ],
   "ignore_index": -100,
   "model_type": "florence2",
   "projection_dim": 1024,

   "architectures": [
     "Florence2ForConditionalGeneration"
   ],
+  "auto_map": {
+    "AutoConfig": "microsoft/Florence-2-base--configuration_florence2.Florence2Config",
+    "AutoModelForCausalLM": "microsoft/Florence-2-base--modeling_florence2.Florence2ForConditionalGeneration"
+  },
   "ignore_index": -100,
   "model_type": "florence2",
   "projection_dim": 1024,