Instructions to use alakxender/florence-base-dv01-01-dv-e10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alakxender/florence-base-dv01-01-dv-e10 with Transformers:

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

pipe = pipeline("image-text-to-text", model="alakxender/florence-base-dv01-01-dv-e10", trust_remote_code=True)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("alakxender/florence-base-dv01-01-dv-e10", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("alakxender/florence-base-dv01-01-dv-e10", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use alakxender/florence-base-dv01-01-dv-e10 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alakxender/florence-base-dv01-01-dv-e10"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alakxender/florence-base-dv01-01-dv-e10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/alakxender/florence-base-dv01-01-dv-e10

SGLang

How to use alakxender/florence-base-dv01-01-dv-e10 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 "alakxender/florence-base-dv01-01-dv-e10" \
    --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": "alakxender/florence-base-dv01-01-dv-e10",
		"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 "alakxender/florence-base-dv01-01-dv-e10" \
        --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": "alakxender/florence-base-dv01-01-dv-e10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use alakxender/florence-base-dv01-01-dv-e10 with Docker Model Runner:
```
docker model run hf.co/alakxender/florence-base-dv01-01-dv-e10
```

Florence Dhivehi

Finetuned florence2-base on dhivehi-text-image data

Downloads last month: 8

Safetensors

Model size

0.3B params

Tensor type

F32

Model tree for alakxender/florence-base-dv01-01-dv-e10

Base model

microsoft/Florence-2-base

Finetuned

(21)

this model

Dataset used to train alakxender/florence-base-dv01-01-dv-e10

Collection including alakxender/florence-base-dv01-01-dv-e10

Vision

Collection

Dhivehi Computer Vision: Image classification, OCR, image-to-text, and text-to-image models for Thaana content • 18 items • Updated Mar 2