Instructions to use WafaaFraih/medical-caption-model-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WafaaFraih/medical-caption-model-v1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="WafaaFraih/medical-caption-model-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("WafaaFraih/medical-caption-model-v1")
model = AutoModelForMultimodalLM.from_pretrained("WafaaFraih/medical-caption-model-v1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use WafaaFraih/medical-caption-model-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/WafaaFraih/medical-caption-model-v1

SGLang

How to use WafaaFraih/medical-caption-model-v1 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/medical-caption-model-v1" \
    --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/medical-caption-model-v1",
		"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/medical-caption-model-v1" \
        --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/medical-caption-model-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WafaaFraih/medical-caption-model-v1 with Docker Model Runner:
```
docker model run hf.co/WafaaFraih/medical-caption-model-v1
```

medical-caption-model-v1

This model is a fine-tuned version of nlpconnect/vit-gpt2-image-captioning on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 5.0189
Bleu: 0.0332
Meteor: 5.8196
Rougel: 10.2378

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Meteor	Rougel
5.1913	2.0	200	5.2321	0.0396	7.0115	10.6658
4.6615	4.0	400	4.9947	0.0495	6.1088	10.9570
4.3567	6.0	600	4.9261	0.0384	5.7674	11.0691
4.0884	8.0	800	4.9632	0.0344	5.9190	10.6889
3.9311	10.0	1000	5.0189	0.0332	5.8196	10.2378

Framework versions

Transformers 4.56.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0

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Safetensors

Model size

0.2B params

Tensor type

F32

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