Instructions to use devendradhakad/autodroid-Abiray-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 devendradhakad/autodroid-Abiray-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 devendradhakad/autodroid-Abiray-Nanbeige4.2-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf devendradhakad/autodroid-Abiray-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 devendradhakad/autodroid-Abiray-Nanbeige4.2-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf devendradhakad/autodroid-Abiray-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 devendradhakad/autodroid-Abiray-Nanbeige4.2-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf devendradhakad/autodroid-Abiray-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 devendradhakad/autodroid-Abiray-Nanbeige4.2-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf devendradhakad/autodroid-Abiray-Nanbeige4.2-3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/devendradhakad/autodroid-Abiray-Nanbeige4.2-3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use devendradhakad/autodroid-Abiray-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 "devendradhakad/autodroid-Abiray-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": "devendradhakad/autodroid-Abiray-Nanbeige4.2-3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/devendradhakad/autodroid-Abiray-Nanbeige4.2-3B-GGUF:Q4_K_M

Ollama
How to use devendradhakad/autodroid-Abiray-Nanbeige4.2-3B-GGUF with Ollama:
```
ollama run hf.co/devendradhakad/autodroid-Abiray-Nanbeige4.2-3B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use devendradhakad/autodroid-Abiray-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 devendradhakad/autodroid-Abiray-Nanbeige4.2-3B-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": "devendradhakad/autodroid-Abiray-Nanbeige4.2-3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use devendradhakad/autodroid-Abiray-Nanbeige4.2-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull devendradhakad/autodroid-Abiray-Nanbeige4.2-3B-GGUF:Q4_K_M

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use devendradhakad/autodroid-Abiray-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 devendradhakad/autodroid-Abiray-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 devendradhakad/autodroid-Abiray-Nanbeige4.2-3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use devendradhakad/autodroid-Abiray-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 devendradhakad/autodroid-Abiray-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 "devendradhakad/autodroid-Abiray-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 Quants

This repository contains GGUF quantizations for Nanbeige/Nanbeige4.2-3B.

Original Model: Nanbeige/Nanbeige4.2-3B
Base Architecture: Looped Transformer (3B non-embedding parameters)
Quantization Format: GGUF (Q4_K_M, Q4_K_S, Q5_K_M, Q6_K, Q8_0)

Available Files & Quantization Details

File Name	Size	Quant Method	Description
`Nanbeige4.2-3B-Q4_K_M.gguf`	~2.57 GB	`Q4_K_M`	4-bit medium. Recommended balance of speed, memory usage, and quality.
`Nanbeige4.2-3B-Q4_K_S.gguf`	~2.50 GB	`Q4_K_S`	4-bit small. Slightly lower memory footprint.
`Nanbeige4.2-3B-Q5_K_M.gguf`	~2.99 GB	`Q5_K_M`	5-bit medium. Higher precision with slight increase in size.
`Nanbeige4.2-3B-Q6_K.gguf`	~3.42 GB	`Q6_K`	6-bit quantization. Very close to FP16 performance.
`Nanbeige4.2-3B-Q8_0.gguf`	~4.43 GB	`Q8_0`	8-bit quantization. Maximum quality for GGUF.

Usage Guide

1. Running with `llama.cpp`

For full support, clone the official or nanbeige42 fork of llama.cpp:

# Clone the repository with Nanbeige support
git clone -b nanbeige42 [https://github.com/Nanbeige/llama.cpp.git](https://github.com/Nanbeige/llama.cpp.git)
cd llama.cpp

# Build with CUDA support
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release -j

# Download a model from this repository
huggingface-cli download Abiray/Nanbeige4.2-3B-GGUF Nanbeige4.2-3B-Q4_K_M.gguf --local-dir .

# Run CLI inference
./build/bin/llama-cli -m Nanbeige4.2-3B-Q4_K_M.gguf -ngl 99 -p "Which number is bigger, 9.11 or 9.8?"

Downloads last month: 64

GGUF

Model size

4B params

Architecture

nanbeige

Hardware compatibility

4-bit

Model tree for devendradhakad/autodroid-Abiray-Nanbeige4.2-3B-GGUF

Base model

Nanbeige/Nanbeige4.2-3B-Base

Finetuned

Nanbeige/Nanbeige4.2-3B

Quantized

(52)

this model

Nanbeige4.2-3B - GGUF Quants

Available Files & Quantization Details

Usage Guide

1. Running with llama.cpp

Model tree for devendradhakad/autodroid-Abiray-Nanbeige4.2-3B-GGUF

1. Running with `llama.cpp`