Instructions to use Image-Captioning-ML/Vit-GPT2-UCA-UCF-06 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-06 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-06")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Image-Captioning-ML/Vit-GPT2-UCA-UCF-06")
model = AutoModelForMultimodalLM.from_pretrained("Image-Captioning-ML/Vit-GPT2-UCA-UCF-06")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Image-Captioning-ML/Vit-GPT2-UCA-UCF-06 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-06"
# 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-06",
		"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-06

SGLang

How to use Image-Captioning-ML/Vit-GPT2-UCA-UCF-06 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-06" \
    --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-06",
		"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-06" \
        --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-06",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Image-Captioning-ML/Vit-GPT2-UCA-UCF-06 with Docker Model Runner:
```
docker model run hf.co/Image-Captioning-ML/Vit-GPT2-UCA-UCF-06
```

Vit-GPT2-UCA-UCF-06

This model is a fine-tuned version of nlpconnect/vit-gpt2-image-captioning on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1937
Rouge1: 29.6433
Rouge2: 8.3589
Rougel: 25.256
Rougelsum: 25.5825
Gen Len: 15.63

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
gradient_accumulation_steps: 4
total_train_batch_size: 16
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
0.8073	0.3258	500	0.1840	31.5942	9.2754	27.0997	27.4879	17.309
0.6562	0.6516	1000	0.1805	31.3758	9.5474	26.788	27.1031	16.271
0.6123	0.9774	1500	0.1795	32.219	9.7783	27.4235	27.7537	16.455
0.5502	1.3030	2000	0.1821	31.0914	9.2688	26.5321	26.8962	15.66
0.5281	1.6288	2500	0.1832	31.0119	9.0876	26.4645	26.7925	16.042
0.5085	1.9546	3000	0.1847	31.0869	9.0206	26.2838	26.6729	16.004
0.4584	2.2802	3500	0.1919	29.6475	8.3551	25.1859	25.455	15.92
0.4536	2.6060	4000	0.1922	30.3476	8.7192	25.8444	26.0811	15.981
0.4477	2.9317	4500	0.1937	29.6433	8.3589	25.256	25.5825	15.63

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

Downloads last month: 1

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-06

Base model

nlpconnect/vit-gpt2-image-captioning

Finetuned

(17)

this model

Finetunes

1 model