Instructions to use vishwa27/GIT_inf_w_caption_blur_ep5_eval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vishwa27/GIT_inf_w_caption_blur_ep5_eval 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_blur_ep5_eval")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("vishwa27/GIT_inf_w_caption_blur_ep5_eval")
model = AutoModelForImageTextToText.from_pretrained("vishwa27/GIT_inf_w_caption_blur_ep5_eval")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use vishwa27/GIT_inf_w_caption_blur_ep5_eval 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_blur_ep5_eval"
# 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_blur_ep5_eval",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vishwa27/GIT_inf_w_caption_blur_ep5_eval

SGLang

How to use vishwa27/GIT_inf_w_caption_blur_ep5_eval 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_blur_ep5_eval" \
    --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_blur_ep5_eval",
		"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_blur_ep5_eval" \
        --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_blur_ep5_eval",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vishwa27/GIT_inf_w_caption_blur_ep5_eval with Docker Model Runner:
```
docker model run hf.co/vishwa27/GIT_inf_w_caption_blur_ep5_eval
```

GIT_inf_w_caption_blur_ep5_eval

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: 0.0688
Rouge1: 11.5483
Rouge2: 6.9038
Rougel: 10.6731
Rougelsum: 10.6966
Gen Len: 217.74

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: 16
eval_batch_size: 8
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
0.0775	1.0	1586	0.0774	11.837	5.6006	10.9944	11.0165	218.44
0.0658	2.0	3172	0.0726	9.7028	5.0964	9.0714	9.0844	218.44
0.0541	3.0	4758	0.0693	11.4449	6.3978	10.5899	10.6179	218.44
0.0432	4.0	6344	0.0682	11.1405	6.5221	10.3109	10.3318	218.39
0.0342	5.0	7930	0.0688	11.5483	6.9038	10.6731	10.6966	217.74

Framework versions

Transformers 4.38.1
Pytorch 2.2.1+cu121
Datasets 2.17.1
Tokenizers 0.15.2

Downloads last month: 7

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_blur_ep5_eval

Base model

microsoft/git-base

Finetuned

(117)

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