Instructions to use sageofai/Qwen25VL-MEDVQA-GI-S1-subtask1-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use sageofai/Qwen25VL-MEDVQA-GI-S1-subtask1-v4 with PEFT:
```
Task type is invalid.
```

How to use sageofai/Qwen25VL-MEDVQA-GI-S1-subtask1-v4 with Transformers:

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

pipe = pipeline("text-generation", model="sageofai/Qwen25VL-MEDVQA-GI-S1-subtask1-v4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sageofai/Qwen25VL-MEDVQA-GI-S1-subtask1-v4", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sageofai/Qwen25VL-MEDVQA-GI-S1-subtask1-v4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sageofai/Qwen25VL-MEDVQA-GI-S1-subtask1-v4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sageofai/Qwen25VL-MEDVQA-GI-S1-subtask1-v4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sageofai/Qwen25VL-MEDVQA-GI-S1-subtask1-v4

SGLang

How to use sageofai/Qwen25VL-MEDVQA-GI-S1-subtask1-v4 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 "sageofai/Qwen25VL-MEDVQA-GI-S1-subtask1-v4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sageofai/Qwen25VL-MEDVQA-GI-S1-subtask1-v4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "sageofai/Qwen25VL-MEDVQA-GI-S1-subtask1-v4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sageofai/Qwen25VL-MEDVQA-GI-S1-subtask1-v4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sageofai/Qwen25VL-MEDVQA-GI-S1-subtask1-v4 with Docker Model Runner:
```
docker model run hf.co/sageofai/Qwen25VL-MEDVQA-GI-S1-subtask1-v4
```

Qwen25VL-MEDVQA-GI-S1-subtask1-v4

File size: 272 Bytes

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torch>=2.1.0
transformers>=4.45.0
accelerate>=0.34.0
peft>=0.13.0
bitsandbytes>=0.43.0
ms-swift==3.8.0
qwen_vl_utils==0.0.11
datasets>=2.20.0
evaluate>=0.4.3
sacrebleu>=2.4.0
rouge_score>=0.1.2
nltk>=3.9.0
Pillow>=10.0.0
tqdm>=4.66.0
numpy>=1.26.0
huggingface_hub>=0.24.0