Text Generation
Transformers
GGUF
English
medical
genomics
cancer
oncology
mutation-analysis
precision-medicine
GGUF
Ollama
conversational
Instructions to use Zero21/OncoScope with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zero21/OncoScope with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Zero21/OncoScope") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Zero21/OncoScope", dtype="auto") - llama-cpp-python
How to use Zero21/OncoScope with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Zero21/OncoScope", filename="oncoscope-gemma-3n-merged.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 Zero21/OncoScope with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Zero21/OncoScope:Q8_0 # Run inference directly in the terminal: llama-cli -hf Zero21/OncoScope:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Zero21/OncoScope:Q8_0 # Run inference directly in the terminal: llama-cli -hf Zero21/OncoScope: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 Zero21/OncoScope:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf Zero21/OncoScope: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 Zero21/OncoScope:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Zero21/OncoScope:Q8_0
Use Docker
docker model run hf.co/Zero21/OncoScope:Q8_0
- LM Studio
- Jan
- vLLM
How to use Zero21/OncoScope with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Zero21/OncoScope" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Zero21/OncoScope", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Zero21/OncoScope:Q8_0
- SGLang
How to use Zero21/OncoScope 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 "Zero21/OncoScope" \ --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": "Zero21/OncoScope", "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 "Zero21/OncoScope" \ --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": "Zero21/OncoScope", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use Zero21/OncoScope with Ollama:
ollama run hf.co/Zero21/OncoScope:Q8_0
- Unsloth Studio new
How to use Zero21/OncoScope 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 Zero21/OncoScope 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 Zero21/OncoScope to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Zero21/OncoScope to start chatting
- Docker Model Runner
How to use Zero21/OncoScope with Docker Model Runner:
docker model run hf.co/Zero21/OncoScope:Q8_0
- Lemonade
How to use Zero21/OncoScope with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Zero21/OncoScope:Q8_0
Run and chat with the model
lemonade run user.OncoScope-Q8_0
List all available models
lemonade list
| FROM ./oncoscope-gemma-3n-merged.Q8_0.gguf | |
| # OncoScope Cancer Genomics Specialist Model | |
| # Fine-tuned for cancer mutation analysis, risk assessment, and therapeutic recommendations | |
| PARAMETER temperature 1.0 | |
| PARAMETER top_k 64 | |
| PARAMETER top_p 0.95 | |
| PARAMETER repeat_penalty 1.0 | |
| PARAMETER num_ctx 4096 | |
| PARAMETER num_predict 512 | |
| PARAMETER stop "<end_of_turn>" | |
| SYSTEM """You are OncoScope, an expert AI assistant specialized in cancer genomics analysis. You provide accurate, evidence-based analysis of cancer mutations, risk assessments, and therapeutic recommendations. | |
| Your expertise includes: | |
| - Cancer mutation pathogenicity assessment using ACMG/AMP guidelines | |
| - Clinical significance determination with confidence scoring | |
| - Therapeutic target identification and drug recommendations | |
| - Risk stratification for hereditary cancer syndromes | |
| - Family history analysis and genetic counseling guidance | |
| - Multi-mutation interaction analysis | |
| You always: | |
| - Provide evidence-based analysis with confidence scores | |
| - Include relevant clinical guidelines and literature references | |
| - Consider patient demographics and family history | |
| - Recommend appropriate genetic counseling when indicated | |
| - Explain complex genomics concepts clearly | |
| - Maintain professional medical terminology while being accessible | |
| You respond in structured JSON format when requested, with clear pathogenicity classifications, risk assessments, and actionable recommendations.""" | |
| TEMPLATE """{{ if .System }}<start_of_turn>user | |
| {{ .System }}<end_of_turn> | |
| <start_of_turn>model | |
| I understand. I'll follow these guidelines for cancer genomics analysis.<end_of_turn> | |
| {{ end }}{{ if .Prompt }}<start_of_turn>user | |
| {{ .Prompt }}<end_of_turn> | |
| <start_of_turn>model | |
| {{ end }}""" |