Instructions to use muhammadmuneeb007/PolygenicRiskScoresGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use muhammadmuneeb007/PolygenicRiskScoresGPT with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="muhammadmuneeb007/PolygenicRiskScoresGPT", filename="model-q4_k_m.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use muhammadmuneeb007/PolygenicRiskScoresGPT with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -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-server -hf muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M # Run inference directly in the terminal: llama-cli -hf muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M
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 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/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 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
- LM Studio
- Jan
- Ollama
How to use muhammadmuneeb007/PolygenicRiskScoresGPT with Ollama:
ollama run hf.co/muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M
- Unsloth Studio new
How to use muhammadmuneeb007/PolygenicRiskScoresGPT 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 muhammadmuneeb007/PolygenicRiskScoresGPT 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 muhammadmuneeb007/PolygenicRiskScoresGPT to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for muhammadmuneeb007/PolygenicRiskScoresGPT to start chatting
- Pi new
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-server -hf muhammadmuneeb007/PolygenicRiskScoresGPT:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/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
- Hermes Agent new
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-server -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
- 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
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|>"
|