Instructions to use WafaaFraih/blip-vqa-base-blip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WafaaFraih/blip-vqa-base-blip with Transformers:

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

pipe = pipeline("image-text-to-text", model="WafaaFraih/blip-vqa-base-blip")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("WafaaFraih/blip-vqa-base-blip")
model = AutoModelForMultimodalLM.from_pretrained("WafaaFraih/blip-vqa-base-blip")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use WafaaFraih/blip-vqa-base-blip with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/WafaaFraih/blip-vqa-base-blip

SGLang

How to use WafaaFraih/blip-vqa-base-blip 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 "WafaaFraih/blip-vqa-base-blip" \
    --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": "WafaaFraih/blip-vqa-base-blip",
		"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 "WafaaFraih/blip-vqa-base-blip" \
        --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": "WafaaFraih/blip-vqa-base-blip",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WafaaFraih/blip-vqa-base-blip with Docker Model Runner:
```
docker model run hf.co/WafaaFraih/blip-vqa-base-blip
```

blip-vqa-base-blip

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

Loss: 0.4584
Wer: 0.8802

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
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: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.853	3.88	50	0.5155	0.8983
0.2416	7.72	100	0.3933	0.8862
0.0888	11.56	150	0.4088	0.8853
0.0402	15.4	200	0.4175	0.8793
0.0238	19.24	250	0.4288	0.8759
0.015	23.08	300	0.4322	0.875
0.0096	26.96	350	0.4346	0.8836
0.006	30.8	400	0.4446	0.8741
0.0039	34.64	450	0.4484	0.8767
0.0027	38.48	500	0.4536	0.8793
0.0019	42.32	550	0.4551	0.8793
0.0016	46.16	600	0.4573	0.8810
0.0014	50.0	650	0.4584	0.8802

Framework versions

Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.2

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Safetensors

Model size

0.2B params

Tensor type

F32

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