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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Image-Captioning-ML/TimeSformer-GPT2-UCF-UCA"
# 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/TimeSformer-GPT2-UCF-UCA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

TimeSformer-GPT2-UCF-UCA

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

epoch: 2.6392
eval_gen_len: 5.9498
eval_loss: 0.2492
eval_rouge1: 19.7623
eval_rouge2: 0.1573
eval_rougeL: 17.2583
eval_rougeLsum: 17.3092
eval_runtime: 1000.9442
eval_samples_per_second: 1.691
eval_steps_per_second: 1.691
step: 21000

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: 1
eval_batch_size: 1
seed: 42
optimizer: Use OptimizerNames.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

Framework versions

Transformers 4.51.1
Pytorch 2.5.1+cu124
Datasets 3.5.0
Tokenizers 0.21.0

Downloads last month: 4

Safetensors

Model size

0.3B params

Tensor type

F32

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Image-Text-to-Text

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