Instructions to use Testament200156/medgemma3-thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Testament200156/medgemma3-thinking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Testament200156/medgemma3-thinking")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Testament200156/medgemma3-thinking", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Testament200156/medgemma3-thinking with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Testament200156/medgemma3-thinking:F16 # Run inference directly in the terminal: llama cli -hf Testament200156/medgemma3-thinking:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Testament200156/medgemma3-thinking:F16 # Run inference directly in the terminal: llama cli -hf Testament200156/medgemma3-thinking:F16
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Testament200156/medgemma3-thinking:F16 # Run inference directly in the terminal: ./llama-cli -hf Testament200156/medgemma3-thinking:F16
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Testament200156/medgemma3-thinking:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Testament200156/medgemma3-thinking:F16
Use Docker
docker model run hf.co/Testament200156/medgemma3-thinking:F16
- LM Studio
- Jan
- vLLM
How to use Testament200156/medgemma3-thinking with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Testament200156/medgemma3-thinking" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Testament200156/medgemma3-thinking", "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/Testament200156/medgemma3-thinking:F16
- SGLang
How to use Testament200156/medgemma3-thinking 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 "Testament200156/medgemma3-thinking" \ --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": "Testament200156/medgemma3-thinking", "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 "Testament200156/medgemma3-thinking" \ --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": "Testament200156/medgemma3-thinking", "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" } } ] } ] }' - Ollama
How to use Testament200156/medgemma3-thinking with Ollama:
ollama run hf.co/Testament200156/medgemma3-thinking:F16
- Unsloth Desktop
- Docker Model Runner
How to use Testament200156/medgemma3-thinking with Docker Model Runner:
docker model run hf.co/Testament200156/medgemma3-thinking:F16
- Lemonade
How to use Testament200156/medgemma3-thinking with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Testament200156/medgemma3-thinking:F16
Run and chat with the model
lemonade run user.medgemma3-thinking-F16
List all available models
lemonade list
- Atomic Chat
Download capture.PNG from Testament200156/medgemma3-thinking: direct link, hf CLI and curl.
- Browser
- Download file 320 kB
-
https://huggingface.co/Testament200156/medgemma3-thinking/resolve/main/capture.PNG
- Command line
-
hf download hf://Testament200156/medgemma3-thinking/capture.PNG
-
curl -L -o capture.PNG https://huggingface.co/Testament200156/medgemma3-thinking/resolve/main/capture.PNG
320 kB
- Xet hash:
- 9f8c95f78818926a7ff7c83a450cf773ebbc2cb65265ed5898f9957e473e8806
- Size of remote file:
- 320 kB
- SHA256:
- a1384c26b7ed751d355cf1544f72a04caf4ee43a23b9e90e29ca9ae8fa8e20f1
·
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