Image-Text-to-Text
Transformers
Safetensors
English
medical
radiology
chest-x-ray
multimodal
report-generation
structured-reporting
impression
lora
medical-imaging
clinical-nlp
conversational
Instructions to use erjui/Lingshu-7b-srrg-impression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use erjui/Lingshu-7b-srrg-impression with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="erjui/Lingshu-7b-srrg-impression") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("erjui/Lingshu-7b-srrg-impression", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use erjui/Lingshu-7b-srrg-impression with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "erjui/Lingshu-7b-srrg-impression" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "erjui/Lingshu-7b-srrg-impression", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/erjui/Lingshu-7b-srrg-impression
- SGLang
How to use erjui/Lingshu-7b-srrg-impression 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 "erjui/Lingshu-7b-srrg-impression" \ --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": "erjui/Lingshu-7b-srrg-impression", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "erjui/Lingshu-7b-srrg-impression" \ --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": "erjui/Lingshu-7b-srrg-impression", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use erjui/Lingshu-7b-srrg-impression with Docker Model Runner:
docker model run hf.co/erjui/Lingshu-7b-srrg-impression
Ctrl+K
- checkpoint-3172
- eval_test
- eval_test_reviewed
- eval_validate
- inference_test
- inference_test_reviewed
- inference_validate
- merged
- 1.7 kB
- 5.47 kB
- 863 Bytes
- 381 MB xet
- 605 Bytes
- 404 Bytes
- 1.02 kB
- 186 Bytes
- 7.36 kB
- 1.44 kB
- 1.43 kB
- 4.23 kB
- 4.23 kB
- 4.23 kB
- 0 Bytes
- 1.67 MB
- 791 Bytes
- 613 Bytes
- 11.4 MB xet
- 4.76 kB
- 38.5 kB
- 238 Bytes
- 56.3 kB
- 5.84 kB xet
- 907 Bytes
- 2.78 MB