Instructions to use komomike/MaringGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use komomike/MaringGPT 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 komomike/MaringGPT
# Run inference directly in the terminal:
llama cli -hf komomike/MaringGPT

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf komomike/MaringGPT
# Run inference directly in the terminal:
llama cli -hf komomike/MaringGPT

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 komomike/MaringGPT
# Run inference directly in the terminal:
./llama-cli -hf komomike/MaringGPT

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 komomike/MaringGPT
# Run inference directly in the terminal:
./build/bin/llama-cli -hf komomike/MaringGPT

Use Docker

docker model run hf.co/komomike/MaringGPT

LM Studio
Jan

vLLM

How to use komomike/MaringGPT with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/komomike/MaringGPT

Ollama
How to use komomike/MaringGPT with Ollama:
```
ollama run hf.co/komomike/MaringGPT
```

Unsloth Studio

How to use komomike/MaringGPT 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 komomike/MaringGPT 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 komomike/MaringGPT to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for komomike/MaringGPT to start chatting

How to use komomike/MaringGPT with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf komomike/MaringGPT

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": "komomike/MaringGPT"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use komomike/MaringGPT with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf komomike/MaringGPT

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 komomike/MaringGPT

Run Hermes

hermes

OpenClaw new

How to use komomike/MaringGPT with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf komomike/MaringGPT

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 "komomike/MaringGPT" \
  --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"

Docker Model Runner
How to use komomike/MaringGPT with Docker Model Runner:
```
docker model run hf.co/komomike/MaringGPT
```

Lemonade

How to use komomike/MaringGPT with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull komomike/MaringGPT

Run and chat with the model

lemonade run user.MaringGPT-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

🌌 MaringGPT

A modern, lightweight AI model built to preserve, translate, and advance digital support for the Maring language.

📜 About the Maring Language & History

"Meiringba" / "Maringa" — People who keep the unquenched fire alive.

Maring (ISO 639-3: nng) is a Sino-Tibetan language spoken primarily by the Maring community in the Tengnoupal and Chandel districts of Manipur, Northeast India.

Ethno-Linguistic Roots: Linguistically classified within the Tibeto-Burman branch, Maring shares unique connections with both Tangkhulic and Kuki-Chin languages.
Cultural Identity: Traditionally an oral-rich language, its speakers are deeply connected to the hills of Southeast Manipur.
The Digital Gap: Like many indigenous languages of Northeast India, Maring remains under-represented in modern computational linguistics and digital datasets. MaringGPT is created to help bridge this gap by bringing native context and modern AI capabilities into a fine-tuned LLM.

📸 Demo Preview

Preview 01	Preview 02

✨ Key Features

🗣️ Language Awareness: Fine-tuned to understand context and structured text associated with Maring language instruction.
⚡ Ultra-Efficient (3B Parameters): Designed on Meta's Llama 3.2 3B architecture, enabling low latency and local execution on consumer GPUs or mobile platforms.
🎯 Instruction-Tuned: Structured dialogue handling via ChatML / Llama-3 system prompts for crisp, natural conversations.

⚙️ Training Setup

The model was fine-tuned using parameter-efficient optimization techniques:

Parameter	Specification
Base Model	`meta-llama/Llama-3.2-3B-Instruct`
Method	QLoRA (4-bit Quantization) via Unsloth
Precision	`bfloat16`
Data Format	Custom conversational instruction-response pairs

🚀 Quickstart

Run MaringGPT locally using Python and the transformers library:

1. Installation

pip install --upgrade transformers torch accelerate

Downloads last month: 10

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for komomike/MaringGPT

Base model

meta-llama/Llama-3.2-3B-Instruct

Quantized

(496)

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