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 / README.md

ramgovindv

Update README.md

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	---
	tags:
	- gguf
	- llama.cpp
	- unsloth
	- mind_call
	- function_call
	datasets:
	- frshafi/mind_call
	language:
	- en
	base_model:
	- meta-llama/Llama-3.2-3B-Instruct
	metrics:
	- accuracy: 88%
	---

	# health_function_call_llama3.2_3b_gguf: GGUF

	A fine-tuned Llama 3.2 3B GGUF model designed for structured function calling in healthcare edge devices.Trained to convert natural
	language health queries into JSON-based function calls.

	Base Model: LLama 3.2 3B

	Fine Tuning: Parameter Efficient Fine Tuning. Targeted all linear layers (Q, K, V, O, gate, up, down), the
	model learned complex mapping logic while maintaining a tiny 10.5 MB adapter footprint.

	Quantization: Exported to GGUF (Q4_K_M) format.

	Dataset: The model is trained on the MindCall Dataset, a curated synthetic collection of 5,000+ high-fidelity health interaction pairs.

	## 🚀 Key Features

	- Converts user queries → structured API calls
	- Lightweight GGUF format (runs locally via llama.cpp)
	- Optimized for deterministic outputs (low temperature)
	- Supports reasoning via `<think>` tags

	## 📦 Model Files

	- `Llama-3.2-3B-Instruct.Q4_K_M.gguf`

	## ⚡ Quick Start (Python)

	### Install dependencies

	```bash
	pip install llama-cpp-python huggingface_hub

	```

	### Load the model
	``` code
	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",
	)
	```

	### Inference
	``` code
	query = "I am feeling dizzy for 2 days"

	prompt = f"""
	You are an API generator.
	Return JSON in this format:
	{{
	"name": "function_name",
	"parameters": {{
	"key": "value"
	}}
	}}
	User query:
	{query}
	JSON:
	"""

	response = llm.create_chat_completion(
	messages=[{"role": "user", "content": prompt}],
	temperature=0.1
	)
	output = response["choices"][0]["message"]["content"]
	print(output)
	```

	## Output
	```code
	<think>
	User has dizziness → likely need blood pressure check
	</think>

	<function>
	{
	"name": "get_blood_pressure_data",
	"parameters": {
	"num_days": 2
	}
	}
	</function>
	```
	```<think>``` → reasoning <br>
	```<function>``` → actual function call



	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)