Instructions to use NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF 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 NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/Mellum2-12B-A2.5B-Instruct-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 NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NANI-Nithin/Mellum2-12B-A2.5B-Instruct-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 NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF:Q4_K_M

Ollama
How to use NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF with Ollama:
```
ollama run hf.co/NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop

How to use NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF: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": "NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mellum2-12B-A2.5B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/Mellum2-12B-A2.5B-Instruct-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 NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF: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 "NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF: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"

Mellum2-12B-A2.5B-Instruct-GGUF

This repository contains GGUF quantizations of JetBrains/Mellum2-12B-A2.5B-Instruct for efficient local inference with llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes.

Model details

Base model: JetBrains/Mellum2-12B-A2.5B-Instruct
Format: GGUF
Architecture: Mellum2
Task type: Instruction-following assistant
Context length: 131,072 tokens
License: Apache 2.0

Available quantizations

File	Quantization	Size	Notes
`Mellum2-12B-A2.5B-Instruct-Q4_K_M.gguf`	Q4_K_M	8.07 GB	Lower-memory deployment with good quality-to-size tradeoff
`Mellum2-12B-A2.5B-Instruct-Q5_K_M.gguf`	Q5_K_M	9.21 GB	Higher-quality 5-bit deployment with a modest size increase

Intended use

This model is intended for:

local chat and assistant workflows
coding assistance
tool-use experiments
CPU-friendly or memory-constrained inference
users who want a balance between quality and memory usage

Notes

These quantizations were produced with llama.cpp. During conversion, some tensors may require fallback quantization depending on the model architecture, which is expected and does not prevent successful inference.

Usage

llama.cpp

./llama-server -m Mellum2-12B-A2.5B-Instruct-Q5_K_M.gguf --jinja --port 8000

Ollama

Create a Modelfile pointing to the GGUF file:

FROM ./Mellum2-12B-A2.5B-Instruct-Q4_K_M.gguf

Then run:

ollama create mellum2-q4 -f Modelfile
ollama run mellum2-q4

License

Released under the same license as the base model: Apache 2.0.

Acknowledgments

Base model: JetBrains/Mellum2-12B-A2.5B-Instruct
Quantization performed with llama.cpp

Downloads last month: 4,861

GGUF

Model size

12B params

Architecture

mellum

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Model tree for NANI-Nithin/Mellum2-12B-A2.5B-Instruct-GGUF

Base model

JetBrains/Mellum2-12B-A2.5B-Instruct

Quantized

(26)

this model