Instructions to use Linov1991/linov-0.6b-ibuanakmed-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Linov1991/linov-0.6b-ibuanakmed-finetune 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 Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Linov1991/linov-0.6b-ibuanakmed-finetune: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 Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Linov1991/linov-0.6b-ibuanakmed-finetune: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 Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M

Use Docker

docker model run hf.co/Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M

LM Studio
Jan

vLLM

How to use Linov1991/linov-0.6b-ibuanakmed-finetune with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Linov1991/linov-0.6b-ibuanakmed-finetune"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Linov1991/linov-0.6b-ibuanakmed-finetune",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M

Ollama
How to use Linov1991/linov-0.6b-ibuanakmed-finetune with Ollama:
```
ollama run hf.co/Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M
```
Unsloth Desktop

How to use Linov1991/linov-0.6b-ibuanakmed-finetune with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Linov1991/linov-0.6b-ibuanakmed-finetune: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": "Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Linov1991/linov-0.6b-ibuanakmed-finetune with Docker Model Runner:
```
docker model run hf.co/Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M
```

Lemonade

How to use Linov1991/linov-0.6b-ibuanakmed-finetune with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M

Run and chat with the model

lemonade run user.linov-0.6b-ibuanakmed-finetune-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Linov1991/linov-0.6b-ibuanakmed-finetune with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Linov1991/linov-0.6b-ibuanakmed-finetune: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 Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Linov1991/linov-0.6b-ibuanakmed-finetune with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Linov1991/linov-0.6b-ibuanakmed-finetune: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 "Linov1991/linov-0.6b-ibuanakmed-finetune: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"

Local Health Assistant 0.6B GGUF

A lightweight Indonesian language model designed for local chatbot experimentation, health education prototyping, and offline AI exploration.

This model is provided in GGUF format, making it suitable for running locally using llama.cpp, Ollama, LM Studio, Jan, and other GGUF-compatible runtimes.

The model is intended to support simple educational conversations in Bahasa Indonesia, especially around mother and child health topics such as pregnancy education, breastfeeding, infant care, nutrition, and community health communication.

Mobile and Offline Usage

This model is provided in GGUF format, which makes it suitable for local and offline inference on devices that support GGUF-compatible runtimes.

Because the model is lightweight, it can be used for experimentation on modest hardware, including laptops and some mobile devices. Once the model file has been downloaded, it can run locally without requiring an internet connection.

Can It Run on Mobile Phones?

Yes. This model can be used on mobile phones through GGUF-compatible mobile applications or llama.cpp-based runtimes.

Possible mobile use cases include:

Offline chatbot experimentation
Local health education assistant
WhatsApp message drafting support
Community health education prototype
AI experimentation without cloud API cost
Local-first AI testing where data stays on the device

Performance may vary depending on the device RAM, processor, battery condition, and the mobile application used.

Recommended Device

Requirement	Recommendation
Device	Android or iOS device with GGUF-compatible app
RAM	At least 4 GB RAM recommended
Storage	At least 1 GB free storage
Internet	Required only for initial model download
Offline use	Supported after the model is downloaded

How to Use on Mobile

General steps:

Install a mobile application that supports GGUF models.
Download the .gguf model file from this repository.
Import or load the model file into the mobile app.
Select the appropriate chat mode or text-generation mode.
Start chatting with the model locally.
After the model is downloaded and loaded, internet connection is not required for basic use.

Example Mobile Workflow

Open the GGUF-compatible mobile app.
Choose Import Model or Add Local Model.
Select the model file:

Downloads last month: 30

GGUF

Model size

0.6B params

Architecture

qwen3

Hardware compatibility

4-bit