Instructions to use NANI-Nithin/Nanbeige4.2-3B-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/Nanbeige4.2-3B-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/Nanbeige4.2-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/Nanbeige4.2-3B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NANI-Nithin/Nanbeige4.2-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/Nanbeige4.2-3B-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 NANI-Nithin/Nanbeige4.2-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NANI-Nithin/Nanbeige4.2-3B-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 NANI-Nithin/Nanbeige4.2-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NANI-Nithin/Nanbeige4.2-3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NANI-Nithin/Nanbeige4.2-3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use NANI-Nithin/Nanbeige4.2-3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NANI-Nithin/Nanbeige4.2-3B-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/Nanbeige4.2-3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NANI-Nithin/Nanbeige4.2-3B-GGUF:Q4_K_M

Ollama
How to use NANI-Nithin/Nanbeige4.2-3B-GGUF with Ollama:
```
ollama run hf.co/NANI-Nithin/Nanbeige4.2-3B-GGUF:Q4_K_M
```

Unsloth Studio

How to use NANI-Nithin/Nanbeige4.2-3B-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 NANI-Nithin/Nanbeige4.2-3B-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 NANI-Nithin/Nanbeige4.2-3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NANI-Nithin/Nanbeige4.2-3B-GGUF to start chatting

How to use NANI-Nithin/Nanbeige4.2-3B-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/Nanbeige4.2-3B-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": "NANI-Nithin/Nanbeige4.2-3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use NANI-Nithin/Nanbeige4.2-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/NANI-Nithin/Nanbeige4.2-3B-GGUF:Q4_K_M
```

Lemonade

How to use NANI-Nithin/Nanbeige4.2-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NANI-Nithin/Nanbeige4.2-3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Nanbeige4.2-3B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use NANI-Nithin/Nanbeige4.2-3B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/Nanbeige4.2-3B-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 NANI-Nithin/Nanbeige4.2-3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use NANI-Nithin/Nanbeige4.2-3B-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/Nanbeige4.2-3B-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/Nanbeige4.2-3B-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"

Nanbeige4.2-3B-GGUF

GGUF quantizations of Nanbeige4.2-3B for use with llama.cpp, KoboldCpp, LM Studio, Jan, Open WebUI, Ollama (via GGUF import), and other GGUF-compatible inference engines.

Base Model: Nanbeige/Nanbeige4.2-3B

About

This repository provides a collection of GGUF quantizations of Nanbeige4.2-3B optimized for local inference across a wide range of hardware configurations.

The model was converted from the original Hugging Face weights to GGUF format using the latest available llama.cpp conversion tools. Both traditional K-quants and importance-aware IQ-quants are included to provide a balance between quality, memory usage, and inference speed.

Available Quantizations

2-bit

Quant	Description
Q2_K	Fastest traditional 2-bit quant
Q2_K_S	Improved 2-bit K quant
IQ2_XXS	Ultra-small IQ quant
IQ2_XS	Small IQ quant
IQ2_S	Balanced IQ quant
IQ2_M	Highest-quality 2-bit IQ quant

3-bit

Quant	Description
IQ3_XXS	Compact 3-bit IQ quant
IQ3_XS	Improved 3-bit IQ quant
IQ3_S	Balanced 3-bit IQ quant
IQ3_M	Highest-quality 3-bit IQ quant
Q3_K_S	Small K quant
Q3_K_M	Recommended quality/size balance
Q3_K_L	Highest-quality 3-bit K quant

4-bit

Quant	Description
IQ4_XS	Efficient 4-bit IQ quant
IQ4_NL	High-quality nonlinear 4-bit quant
Q4_0	Legacy 4-bit quant
Q4_1	Improved legacy 4-bit quant
Q4_K_S	Small K quant
Q4_K_M	Recommended 4-bit quant

5-bit

Quant	Description
Q5_K_S	Small 5-bit K quant
Q5_K_M	Recommended 5-bit K quant

6-bit

Quant	Description
Q6_K	High-quality 6-bit quant

8-bit

Quant	Description
Q8_0	Near-lossless quantization

Full Precision

Quant	Description
BF16	Original BF16 GGUF conversion

Recommended Quantizations

For 4 GB RAM

IQ2_M
Q2_K_S

For 6 GB RAM

IQ3_M
Q3_K_M

For 8 GB RAM

IQ4_NL
Q4_K_M

For Maximum Quality

Q6_K
Q8_0
BF16

Usage with llama.cpp

./llama-cli \
  -m Nanbeige4.2-3B-Q4_K_M.gguf \
  -p "Explain quantum computing in simple terms."

LM Studio

Download your preferred GGUF file.
Place it in the LM Studio models directory.
Refresh models.
Load and start chatting.

Ollama

Create a Modelfile:

FROM ./Nanbeige4.2-3B-Q4_K_M.gguf

Then:

ollama create nanbeige4-3b -f Modelfile

Run:

ollama run nanbeige4-3b

Quantization Notes

K-Quants were generated using llama-quantize.
IQ-Quants were generated using importance matrix (imatrix) quantization for improved quality retention at lower bitrates.
Quantizations were produced using the latest available llama.cpp release at build time.

Credits

Base Model

All model weights, architecture, training, and tokenizer credits belong to the original authors of:

Nanbeige/Nanbeige4.2-3B

GGUF Conversion & Quantization

Converted and quantized for the community by:

Nithin Sai Kumar (NANI-Nithin)

Disclaimer

This repository only provides converted and quantized GGUF files. Please refer to the original model repository for:

Training details
Evaluation results
License information
Intended use guidance
Safety considerations

Users must comply with the original model license and usage restrictions.

Support the Original Authors

If you find this model useful, please consider supporting the original creators by starring, downloading, and contributing feedback to the original repository:

https://huggingface.co/Nanbeige/Nanbeige4.2-3B

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GGUF

Model size

4B params

Architecture

nanbeige

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for NANI-Nithin/Nanbeige4.2-3B-GGUF

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Finetuned

Nanbeige/Nanbeige4.2-3B

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