Instructions to use NANI-Nithin/granite-4.2-8b-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/granite-4.2-8b-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/granite-4.2-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/granite-4.2-8b-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/granite-4.2-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/granite-4.2-8b-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/granite-4.2-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NANI-Nithin/granite-4.2-8b-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/granite-4.2-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NANI-Nithin/granite-4.2-8b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NANI-Nithin/granite-4.2-8b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use NANI-Nithin/granite-4.2-8b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NANI-Nithin/granite-4.2-8b-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/granite-4.2-8b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NANI-Nithin/granite-4.2-8b-GGUF:Q4_K_M

Ollama
How to use NANI-Nithin/granite-4.2-8b-GGUF with Ollama:
```
ollama run hf.co/NANI-Nithin/granite-4.2-8b-GGUF:Q4_K_M
```
Unsloth Desktop

How to use NANI-Nithin/granite-4.2-8b-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/granite-4.2-8b-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/granite-4.2-8b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use NANI-Nithin/granite-4.2-8b-GGUF with Docker Model Runner:
```
docker model run hf.co/NANI-Nithin/granite-4.2-8b-GGUF:Q4_K_M
```

Lemonade

How to use NANI-Nithin/granite-4.2-8b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NANI-Nithin/granite-4.2-8b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.granite-4.2-8b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use NANI-Nithin/granite-4.2-8b-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/granite-4.2-8b-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/granite-4.2-8b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use NANI-Nithin/granite-4.2-8b-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/granite-4.2-8b-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/granite-4.2-8b-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"

Granite 4.2 8B · GGUF Quantizations

Ready-to-run GGUF quantizations of IBM Granite 4.2 8B for local inference with llama.cpp, Ollama, LM Studio, Open WebUI, and other GGUF-compatible runtimes.

This repository provides a complete range of quantization formats, from near-lossless BF16 and Q8 models to highly compressed IQ variants for memory-constrained devices.

About the Base Model

Granite 4.2 8B is IBM's dense reasoning language model designed for agentic workflows, coding, tool use, structured outputs, and enterprise-grade reasoning.

Key Properties

Property	Value
Base Model	ibm-granite/granite-4.2-8b
Parameters	~8B
Architecture	GraniteForCausalLM
Context Window	128K tokens
Reasoning Mode	Switchable thinking / non-thinking
Tool Calling	Supported
License	Apache 2.0
Format	GGUF

Features

🧠 Native reasoning with optional <think> mode
🛠️ Tool calling and agent workflows
💻 Strong coding and software engineering capabilities
📊 Structured JSON generation
⚡ Compatible with llama.cpp and GGUF ecosystems
📱 Efficient local deployment on consumer hardware
🌍 Multilingual support

Recommended Quantizations

Quant	Recommendation
Q6_K	Highest quality without full precision
Q5_K_M	Excellent balance of quality and VRAM
Q4_K_M	Default recommendation for most users
IQ4_XS	Compact deployment with good quality
Q3_K_M	Low-memory environments
Q2_K	Maximum compression

Usage with llama.cpp

./llama-cli \
  -m granite-4.2-8b-Q4_K_M.gguf \
  -c 8192 \
  -p "Explain transformer attention."

Prompt Format

<|im_start|>system
You are a helpful assistant.
<|im_end|>

<|im_start|>user
Explain transformer attention.
<|im_end|>

<|im_start|>assistant

Hardware Guidance

Quant	Approximate RAM / VRAM
Q2_K	4 GB
Q3_K_M	5 GB
Q4_K_M	6 GB
Q5_K_M	7 GB
Q6_K	9 GB
Q8_0	11 GB
BF16	18+ GB

Actual memory consumption depends on context length, batch size, and runtime configuration.

Intended Use

This model is suitable for:

Conversational AI
Coding assistants
Software engineering agents
Retrieval-Augmented Generation (RAG)
Local AI deployments
Research and experimentation
Tool-calling workflows

Limitations

Quantization may slightly reduce model quality compared to the original BF16 checkpoint.
Smaller quantizations prioritize memory efficiency over accuracy.
Model outputs may contain inaccuracies or hallucinations.
Performance depends heavily on the selected quantization level and hardware.

License

This repository distributes quantized versions of IBM Granite 4.2 8B.

The original model is licensed under Apache 2.0. Please refer to the upstream model card for complete licensing and usage information.

Acknowledgements

IBM Granite Team for the original Granite 4.2 model.
llama.cpp contributors for GGUF support and quantization tooling.
Hugging Face for model hosting and distribution.

Citation

If you use these quantizations in research or production, please cite the original Granite 4.2 model from IBM.

Downloads last month: 1,039

GGUF

Model size

9B params

Architecture

granite

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

16-bit

Model tree for NANI-Nithin/granite-4.2-8b-GGUF

Base model

ibm-granite/granite-4.1-8b-base

Finetuned

ibm-granite/granite-4.2-8b

Quantized

(51)

this model