Instructions to use SOSI1004/blip-image-captioning-base-kag100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SOSI1004/blip-image-captioning-base-kag100 with Transformers:

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

pipe = pipeline("image-text-to-text", model="SOSI1004/blip-image-captioning-base-kag100")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("SOSI1004/blip-image-captioning-base-kag100")
model = AutoModelForImageTextToText.from_pretrained("SOSI1004/blip-image-captioning-base-kag100")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SOSI1004/blip-image-captioning-base-kag100 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SOSI1004/blip-image-captioning-base-kag100"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SOSI1004/blip-image-captioning-base-kag100",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SOSI1004/blip-image-captioning-base-kag100

SGLang

How to use SOSI1004/blip-image-captioning-base-kag100 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 "SOSI1004/blip-image-captioning-base-kag100" \
    --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": "SOSI1004/blip-image-captioning-base-kag100",
		"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 "SOSI1004/blip-image-captioning-base-kag100" \
        --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": "SOSI1004/blip-image-captioning-base-kag100",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SOSI1004/blip-image-captioning-base-kag100 with Docker Model Runner:
```
docker model run hf.co/SOSI1004/blip-image-captioning-base-kag100
```

blip-image-captioning-base-kag100

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

Loss: 0.1444
Wer Score: 0.7172

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Score
0.8613	0.9259	500	0.1123	0.7625
0.079	1.8519	1000	0.1131	0.7234
0.055	2.7778	1500	0.1228	0.7312
0.0389	3.7037	2000	0.1313	0.7531
0.0277	4.6296	2500	0.1360	0.7469
0.0205	5.5556	3000	0.1367	0.7188
0.0167	6.4815	3500	0.1410	0.7188
0.0154	7.4074	4000	0.1405	0.7188
0.0145	8.3333	4500	0.1435	0.7172
0.0137	9.2593	5000	0.1444	0.7172

Framework versions

Transformers 4.40.2
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 4

Safetensors

Model size

0.2B params

Tensor type

F32

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Image-Text-to-Text

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