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

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

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

health_function_call_llama3.2_3b_gguf / pyproject.toml

ramgovindv

Create pyproject.toml

4d0fbef verified 2 months ago

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	[build-system]
	requires = ["setuptools>=61"]
	build-backend = "setuptools.backends.legacy:build"

	[project]
	name = "health-function-lm"
	version = "0.1.0"
	description = "Easy inference wrapper for the health function-calling LLaMA 3.2 GGUF model."
	readme = "README.md"
	requires-python = ">=3.9"
	license = { text = "MIT" }
	dependencies = [
	"llama-cpp-python>=0.2.0",
	]

	[project.optional-dependencies]
	ui = ["gradio>=4.0"]

	[project.scripts]
	health-fn = "health_function_lm.cli:main"

	[tool.setuptools.packages.find]
	where = ["."]
	include = ["health_function_lm*"]