Instructions to use ramgovindv/health_function_call_llama3.2_3b_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use ramgovindv/health_function_call_llama3.2_3b_gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ramgovindv/health_function_call_llama3.2_3b_gguf", filename="Llama-3.2-3B-Instruct.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 Settings
- llama.cpp
How to use ramgovindv/health_function_call_llama3.2_3b_gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ramgovindv/health_function_call_llama3.2_3b_gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf ramgovindv/health_function_call_llama3.2_3b_gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ramgovindv/health_function_call_llama3.2_3b_gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf ramgovindv/health_function_call_llama3.2_3b_gguf: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 ramgovindv/health_function_call_llama3.2_3b_gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ramgovindv/health_function_call_llama3.2_3b_gguf: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 ramgovindv/health_function_call_llama3.2_3b_gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ramgovindv/health_function_call_llama3.2_3b_gguf:Q4_K_M
Use Docker
docker model run hf.co/ramgovindv/health_function_call_llama3.2_3b_gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use ramgovindv/health_function_call_llama3.2_3b_gguf with Ollama:
ollama run hf.co/ramgovindv/health_function_call_llama3.2_3b_gguf:Q4_K_M
- Unsloth Studio
How to use ramgovindv/health_function_call_llama3.2_3b_gguf 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 ramgovindv/health_function_call_llama3.2_3b_gguf 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 ramgovindv/health_function_call_llama3.2_3b_gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ramgovindv/health_function_call_llama3.2_3b_gguf to start chatting
- Pi
How to use ramgovindv/health_function_call_llama3.2_3b_gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf ramgovindv/health_function_call_llama3.2_3b_gguf: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": "ramgovindv/health_function_call_llama3.2_3b_gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ramgovindv/health_function_call_llama3.2_3b_gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf ramgovindv/health_function_call_llama3.2_3b_gguf: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 ramgovindv/health_function_call_llama3.2_3b_gguf:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use ramgovindv/health_function_call_llama3.2_3b_gguf with Docker Model Runner:
docker model run hf.co/ramgovindv/health_function_call_llama3.2_3b_gguf:Q4_K_M
- Lemonade
How to use ramgovindv/health_function_call_llama3.2_3b_gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ramgovindv/health_function_call_llama3.2_3b_gguf:Q4_K_M
Run and chat with the model
lemonade run user.health_function_call_llama3.2_3b_gguf-Q4_K_M
List all available models
lemonade list
| """ | |
| Command-line interface for health-function-lm. | |
| Usage | |
| ----- | |
| health-fn "Log my blood pressure as 120/80" | |
| health-fn --pretty "What is my resting heart rate?" | |
| echo "Log weight 70kg" | health-fn | |
| """ | |
| import argparse | |
| import json | |
| import sys | |
| from .model import HealthFunctionLM | |
| def main(): | |
| parser = argparse.ArgumentParser( | |
| prog="health-fn", | |
| description="Run a health query through the function-calling model.", | |
| ) | |
| parser.add_argument( | |
| "query", | |
| nargs="?", | |
| help="Natural-language health query. Reads from stdin if omitted.", | |
| ) | |
| parser.add_argument( | |
| "--pretty", | |
| action="store_true", | |
| default=True, | |
| help="Pretty-print JSON output (default: on).", | |
| ) | |
| parser.add_argument( | |
| "--repo-id", | |
| default="ramgovindv/health_function_call_llama3.2_3b_gguf", | |
| help="Hugging Face repo ID.", | |
| ) | |
| parser.add_argument( | |
| "--filename", | |
| default="Llama-3.2-3B-Instruct.Q4_K_M.gguf", | |
| help="GGUF filename inside the repo.", | |
| ) | |
| parser.add_argument( | |
| "--n-ctx", type=int, default=2048, help="Context window size." | |
| ) | |
| parser.add_argument( | |
| "--n-threads", type=int, default=4, help="Number of CPU threads." | |
| ) | |
| args = parser.parse_args() | |
| query = args.query or sys.stdin.read().strip() | |
| if not query: | |
| parser.error("Provide a query as an argument or via stdin.") | |
| lm = HealthFunctionLM( | |
| repo_id=args.repo_id, | |
| filename=args.filename, | |
| n_ctx=args.n_ctx, | |
| n_threads=args.n_threads, | |
| ) | |
| result = lm.query(query) | |
| indent = 2 if args.pretty else None | |
| print(json.dumps(result, indent=indent)) | |
| if __name__ == "__main__": | |
| main() |