Instructions to use BEncoderRT/medical_inference with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use BEncoderRT/medical_inference with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="BEncoderRT/medical_inference", filename="unsloth.Q8_0.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use BEncoderRT/medical_inference with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf BEncoderRT/medical_inference:Q8_0 # Run inference directly in the terminal: llama-cli -hf BEncoderRT/medical_inference:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf BEncoderRT/medical_inference:Q8_0 # Run inference directly in the terminal: llama-cli -hf BEncoderRT/medical_inference:Q8_0
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 BEncoderRT/medical_inference:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf BEncoderRT/medical_inference:Q8_0
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 BEncoderRT/medical_inference:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf BEncoderRT/medical_inference:Q8_0
Use Docker
docker model run hf.co/BEncoderRT/medical_inference:Q8_0
- LM Studio
- Jan
- vLLM
How to use BEncoderRT/medical_inference with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BEncoderRT/medical_inference" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BEncoderRT/medical_inference", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/BEncoderRT/medical_inference:Q8_0
- Ollama
How to use BEncoderRT/medical_inference with Ollama:
ollama run hf.co/BEncoderRT/medical_inference:Q8_0
- Unsloth Studio new
How to use BEncoderRT/medical_inference with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for BEncoderRT/medical_inference to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for BEncoderRT/medical_inference to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for BEncoderRT/medical_inference to start chatting
- Docker Model Runner
How to use BEncoderRT/medical_inference with Docker Model Runner:
docker model run hf.co/BEncoderRT/medical_inference:Q8_0
- Lemonade
How to use BEncoderRT/medical_inference with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull BEncoderRT/medical_inference:Q8_0
Run and chat with the model
lemonade run user.medical_inference-Q8_0
List all available models
lemonade list
Update README.md
Browse files
README.md
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pipeline_tag: text-generation
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tags:
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# DeepSeek-R1 Medical Reasoning Model
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2. **如果包块是固体性包块并且对周围组织有压迫作用**:这时手术就变得必要了。通常需要进行包块切除术,以解除压迫,缓解症状。
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请尽快安排进行腹部超声检查,并根据检查结果制定最合适的手术方案。<|end▁of▁sentence|>
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pipeline_tag: text-generation
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tags:
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# DeepSeek-R1 Medical Reasoning Model
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2. **如果包块是固体性包块并且对周围组织有压迫作用**:这时手术就变得必要了。通常需要进行包块切除术,以解除压迫,缓解症状。
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请尽快安排进行腹部超声检查,并根据检查结果制定最合适的手术方案。<|end▁of▁sentence|>
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