Instructions to use muhammadmuneeb007/PolygenicRiskScoresGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use muhammadmuneeb007/PolygenicRiskScoresGPT 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 muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M # Run inference directly in the terminal: llama cli -hf muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M # Run inference directly in the terminal: llama cli -hf muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M
Use Docker
docker model run hf.co/muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use muhammadmuneeb007/PolygenicRiskScoresGPT with Ollama:
ollama run hf.co/muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M
- Unsloth Desktop
- Pi
How to use muhammadmuneeb007/PolygenicRiskScoresGPT with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use muhammadmuneeb007/PolygenicRiskScoresGPT with Docker Model Runner:
docker model run hf.co/muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M
- Lemonade
How to use muhammadmuneeb007/PolygenicRiskScoresGPT with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M
Run and chat with the model
lemonade run user.PolygenicRiskScoresGPT-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use muhammadmuneeb007/PolygenicRiskScoresGPT with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use muhammadmuneeb007/PolygenicRiskScoresGPT with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
File size: 1,231 Bytes
f8f6ab2 da3bd6a f8f6ab2 da3bd6a f8f6ab2 da3bd6a f8f6ab2 da3bd6a f8f6ab2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | FROM ./qwen-model-f16.gguf
TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""
SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful AI assistant specialized in polygenic risk score (PRS) analysis and related genomic tools. You provide clear, accurate, and practical information about:
- Calculating and interpreting polygenic risk scores
- Using PRS tools like PRSice-2, PLINK, and LDpred
- Understanding GWAS summary statistics and their application
- Quality control procedures for genetic data
- Population structure and ancestry considerations in PRS
- Cross-ancestry portability of polygenic scores
- Best practices for PRS validation and evaluation
- Interpreting PRS results in clinical and research contexts
- Data formats and file preparation for PRS analysis
- Statistical concepts related to polygenic architecture
Always provide specific, actionable advice with examples when possible. If you're unsure about something, clearly state your limitations rather than guessing."""
PARAMETER temperature 0.7
PARAMETER top_p 0.8
PARAMETER top_k 40
PARAMETER repeat_penalty 1.05
PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"
|