Instructions to use YxBxRyXJx/Florence-2-OD-COCO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YxBxRyXJx/Florence-2-OD-COCO with Transformers:

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

pipe = pipeline("image-text-to-text", model="YxBxRyXJx/Florence-2-OD-COCO", trust_remote_code=True)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("YxBxRyXJx/Florence-2-OD-COCO", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("YxBxRyXJx/Florence-2-OD-COCO", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use YxBxRyXJx/Florence-2-OD-COCO with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/YxBxRyXJx/Florence-2-OD-COCO

SGLang

How to use YxBxRyXJx/Florence-2-OD-COCO 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 "YxBxRyXJx/Florence-2-OD-COCO" \
    --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": "YxBxRyXJx/Florence-2-OD-COCO",
		"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 "YxBxRyXJx/Florence-2-OD-COCO" \
        --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": "YxBxRyXJx/Florence-2-OD-COCO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use YxBxRyXJx/Florence-2-OD-COCO with Docker Model Runner:
```
docker model run hf.co/YxBxRyXJx/Florence-2-OD-COCO
```

Florence-2-OD-COCO

This model is a fine-tuned version of microsoft/Florence-2-large-ft on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.0275

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
3.7209	1.0	200	3.1739
2.9488	2.0	400	3.0275

Framework versions

Transformers 4.45.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.20.1

Downloads last month: 9

Safetensors

Model size

0.8B params

Tensor type

F32

Model tree for YxBxRyXJx/Florence-2-OD-COCO

Base model

microsoft/Florence-2-large-ft

Finetuned

(9)

this model