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.
- Transformers
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
aggressive: requirements.txt
Browse files- requirements.txt +16 -0
requirements.txt
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torch>=2.1.0
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transformers>=4.45.0
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accelerate>=0.34.0
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peft>=0.13.0
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bitsandbytes>=0.43.0
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ms-swift==3.8.0
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qwen_vl_utils==0.0.11
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datasets>=2.20.0
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evaluate>=0.4.3
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sacrebleu>=2.4.0
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rouge_score>=0.1.2
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nltk>=3.9.0
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Pillow>=10.0.0
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tqdm>=4.66.0
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numpy>=1.26.0
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huggingface_hub>=0.24.0
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