Instructions to use vishwa27/GIT_inf_w_caption_ep5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vishwa27/GIT_inf_w_caption_ep5 with Transformers:

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

pipe = pipeline("image-text-to-text", model="vishwa27/GIT_inf_w_caption_ep5")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("vishwa27/GIT_inf_w_caption_ep5")
model = AutoModelForImageTextToText.from_pretrained("vishwa27/GIT_inf_w_caption_ep5")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use vishwa27/GIT_inf_w_caption_ep5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vishwa27/GIT_inf_w_caption_ep5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vishwa27/GIT_inf_w_caption_ep5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vishwa27/GIT_inf_w_caption_ep5

SGLang

How to use vishwa27/GIT_inf_w_caption_ep5 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 "vishwa27/GIT_inf_w_caption_ep5" \
    --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": "vishwa27/GIT_inf_w_caption_ep5",
		"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 "vishwa27/GIT_inf_w_caption_ep5" \
        --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": "vishwa27/GIT_inf_w_caption_ep5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vishwa27/GIT_inf_w_caption_ep5 with Docker Model Runner:
```
docker model run hf.co/vishwa27/GIT_inf_w_caption_ep5
```

GIT_inf_w_caption_ep5

This model is a fine-tuned version of microsoft/git-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0654
Rouge1: 12.7519
Rouge2: 7.5439
Rougel: 11.7555
Rougelsum: 11.7932
Gen Len: 217.52

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: 32
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
0.0748	1.0	793	0.0728	12.808	6.3869	11.7383	11.7726	218.414
0.0609	2.0	1586	0.0680	11.2577	6.0559	10.3475	10.3572	218.404
0.0502	3.0	2379	0.0653	11.9383	6.7699	10.9829	10.9964	218.39
0.0401	4.0	3172	0.0647	12.5115	7.3787	11.4956	11.5161	218.224
0.0328	5.0	3965	0.0654	12.7519	7.5439	11.7555	11.7932	217.52

Framework versions

Transformers 4.38.1
Pytorch 2.2.1+cu121
Datasets 2.17.1
Tokenizers 0.15.2

Downloads last month: 10

Safetensors

Model size

0.2B params

Tensor type

F32

Inference Providers NEW

Image-Text-to-Text

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Model tree for vishwa27/GIT_inf_w_caption_ep5

Base model

microsoft/git-base

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