Instructions to use AlexZigma/timesformer-bert-video-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlexZigma/timesformer-bert-video-captioning with Transformers:

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

pipe = pipeline("image-text-to-text", model="AlexZigma/timesformer-bert-video-captioning")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("AlexZigma/timesformer-bert-video-captioning")
model = AutoModelForMultimodalLM.from_pretrained("AlexZigma/timesformer-bert-video-captioning")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AlexZigma/timesformer-bert-video-captioning with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AlexZigma/timesformer-bert-video-captioning

SGLang

How to use AlexZigma/timesformer-bert-video-captioning 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 "AlexZigma/timesformer-bert-video-captioning" \
    --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": "AlexZigma/timesformer-bert-video-captioning",
		"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 "AlexZigma/timesformer-bert-video-captioning" \
        --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": "AlexZigma/timesformer-bert-video-captioning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AlexZigma/timesformer-bert-video-captioning with Docker Model Runner:
```
docker model run hf.co/AlexZigma/timesformer-bert-video-captioning
```

timesformer-bert-video-captioning

This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.2821
Rouge1: 30.0468
Rouge2: 8.4998
Rougel: 29.0632
Rougelsum: 29.0231
Bleu: 4.8298
Gen Len: 9.5332

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: 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	Bleu	Gen Len	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
2.4961	0.12	200	1.5879	9.5332	1.6548	25.4717	5.11	24.6679	24.6696
1.6561	0.25	400	2.3515	9.5332	1.5339	26.1748	5.9106	25.413	25.3958
1.5772	0.37	600	2.266	9.5332	1.4510	28.6891	6.0431	27.7387	27.8043
1.492	0.49	800	3.6517	9.5332	1.3760	29.0257	7.8515	28.3142	28.3036
1.4736	0.61	1000	3.4866	9.5332	1.3425	27.9774	6.2175	26.7783	26.7207
1.3856	0.74	1200	3.1649	9.5332	1.3118	27.3532	6.5569	26.4964	26.5087
1.3972	0.86	1400	3.5337	9.5332	1.2868	28.233	7.6471	27.3651	27.3354
1.374	0.98	1600	3.5737	9.5332	1.2571	28.8216	7.542	27.9166	27.9353
1.2207	1.1	1800	3.7983	9.5332	1.3362	29.9574	8.1088	28.8866	28.855
1.1861	1.23	2000	3.6521	9.5332	1.3295	30.072	7.7799	28.8417	28.864
1.1173	1.35	2200	3.9784	9.5332	1.3335	29.736	7.9661	28.6877	28.6974
1.1255	1.47	2400	4.3021	9.5332	1.3097	29.8176	8.4656	28.958	28.9571
1.0909	1.6	2600	1.3095	30.0233	8.4896	29.2562	29.2375	4.4782	9.5332
1.1205	1.72	2800	1.2992	29.7164	8.007	28.5027	28.5018	4.44	9.5332
1.1069	1.84	3000	1.2830	29.851	8.4312	28.8139	28.8205	4.6065	9.5332
1.076	1.96	3200	1.2821	30.0468	8.4998	29.0632	29.0231	4.8298	9.5332

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

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