Instructions to use Image-Captioning-ML/Vit-GPT2-UCA-UCF-02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Image-Captioning-ML/Vit-GPT2-UCA-UCF-02 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Image-Captioning-ML/Vit-GPT2-UCA-UCF-02")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Image-Captioning-ML/Vit-GPT2-UCA-UCF-02")
model = AutoModelForMultimodalLM.from_pretrained("Image-Captioning-ML/Vit-GPT2-UCA-UCF-02")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Image-Captioning-ML/Vit-GPT2-UCA-UCF-02 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Image-Captioning-ML/Vit-GPT2-UCA-UCF-02"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Image-Captioning-ML/Vit-GPT2-UCA-UCF-02",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Image-Captioning-ML/Vit-GPT2-UCA-UCF-02

SGLang

How to use Image-Captioning-ML/Vit-GPT2-UCA-UCF-02 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 "Image-Captioning-ML/Vit-GPT2-UCA-UCF-02" \
    --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": "Image-Captioning-ML/Vit-GPT2-UCA-UCF-02",
		"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 "Image-Captioning-ML/Vit-GPT2-UCA-UCF-02" \
        --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": "Image-Captioning-ML/Vit-GPT2-UCA-UCF-02",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Image-Captioning-ML/Vit-GPT2-UCA-UCF-02 with Docker Model Runner:
```
docker model run hf.co/Image-Captioning-ML/Vit-GPT2-UCA-UCF-02
```

Vit-GPT2-UCA-UCF-02

This model is a fine-tuned version of NourFakih/Vit-GPT2-COCO2017Flickr-85k-09 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3246
Rouge1: 27.3344
Rouge2: 7.7352
Rougel: 22.9284
Rougelsum: 23.5349
Gen Len: 16.3847

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: 5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	0.3817	200	0.2663	26.7433	8.0771	22.7209	23.3892	14.8667
No log	0.7634	400	0.2620	28.6185	8.4345	24.4394	25.0197	15.8597
0.1365	1.1450	600	0.2796	25.8005	7.4354	21.8479	22.4492	15.8208
0.1365	1.5267	800	0.2918	26.9003	7.9138	22.5044	22.9753	16.9556
0.0755	1.9084	1000	0.2875	27.4792	7.0944	23.1894	23.7369	16.2569
0.0755	2.2901	1200	0.3187	27.6562	7.6185	23.0095	23.5045	16.8181
0.0755	2.6718	1400	0.3246	27.3344	7.7352	22.9284	23.5349	16.3847

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

Downloads last month: 2

Safetensors

Model size

0.2B params

Tensor type

F32

Inference Providers NEW

Image-Text-to-Text

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for Image-Captioning-ML/Vit-GPT2-UCA-UCF-02

Unable to build the model tree, the base model loops to the model itself. Learn more.