Instructions to use Image-Captioning-ML/Vit-GPT2-UCA-UCF-01 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-01 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-01")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Image-Captioning-ML/Vit-GPT2-UCA-UCF-01")
model = AutoModelForMultimodalLM.from_pretrained("Image-Captioning-ML/Vit-GPT2-UCA-UCF-01")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Image-Captioning-ML/Vit-GPT2-UCA-UCF-01 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-01"
# 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-01",
		"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-01

SGLang

How to use Image-Captioning-ML/Vit-GPT2-UCA-UCF-01 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-01" \
    --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-01",
		"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-01" \
        --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-01",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Image-Captioning-ML/Vit-GPT2-UCA-UCF-01 with Docker Model Runner:
```
docker model run hf.co/Image-Captioning-ML/Vit-GPT2-UCA-UCF-01
```

Vit-GPT2-UCA-UCF-01

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: 0.0808
Rouge1: 57.3757
Rouge2: 45.4285
Rougel: 54.5391
Rougelsum: 54.8713
Gen Len: 15.7384

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	0.3604	200	0.1535	39.243	22.9624	35.1916	35.6871	15.2289
No log	0.7207	400	0.1014	42.1983	25.2146	38.1711	38.7239	15.9897
0.0777	1.0811	600	0.0907	46.7294	31.005	42.8959	43.3674	15.8967
0.0777	1.4414	800	0.0861	50.849	36.5323	47.6738	48.0898	16.2324
0.0642	1.8018	1000	0.0835	52.9082	39.1634	49.4618	50.0549	15.6093
0.0642	2.1622	1200	0.0837	55.0496	42.0646	52.1721	52.5506	16.1463
0.0642	2.5225	1400	0.0824	57.0383	44.9584	53.9845	54.3247	15.9880
0.039	2.8829	1600	0.0808	57.3757	45.4285	54.5391	54.8713	15.7384

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

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