Instructions to use Tdamre/Nanbeige4.2-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tdamre/Nanbeige4.2-3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Tdamre/Nanbeige4.2-3B-GGUF",
	filename="Nanbeige4.2-3B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Use Docker

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

LM Studio
Jan

vLLM

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

Use Docker

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

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

Unsloth Studio

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

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

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Tdamre/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 Tdamre/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 Tdamre/Nanbeige4.2-3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Tdamre/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 Tdamre/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 "Tdamre/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"

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

Lemonade

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

Pull the model

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

Nanbeige4.2-3B GGUF

Unofficial GGUF conversions of Nanbeige/Nanbeige4.2-3B, pinned to source revision 451ed48c3273ecef7ea8faaa43c31ce529763bb1.

Nanbeige4.2-3B is not an ordinary Llama stack. It reuses 22 physical decoder layers for two loops, applies the shared final RMSNorm at the loop boundary, and needs 44 logical KV-cache slots. These files therefore use the custom GGUF architecture name nanbeige and require the included llama.cpp patch.

Files

File	Size	SHA-256
`Nanbeige4.2-3B-Q4_K_M.gguf`	2,574,807,840 bytes	`99c7bfb88907f7eee0a04c4314f1c46bca391819478d8cb90b3e164f09576489`
`Nanbeige4.2-3B-Q8_0.gguf`	4,434,787,104 bytes	`32b60cd0fb3da4d8a3c01d0ca7d0461818c29c81ffb42f8fc71a94141bd803c6`
`llama.cpp-nanbeige.patch`	10,137 bytes	`1177badb8f8f6228052d2f67a9eabe81f2ce69f3ea0b92cf7b1ea2352204284a`

The source checkpoint contains 4,169,800,704 physical parameters. The Q4_K_M file is about 4.93 bits per weight; Q8_0 is about 8.51 bits per weight.

Runtime

Apply the patch to the exact tested llama.cpp revision:

git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
git checkout cf512566dc89d7a2bdefd43f6d4b85a1c18e93a7
git apply /path/to/llama.cpp-nanbeige.patch
cmake -B build
cmake --build build --config Release

Then run either quant:

./build/bin/llama-cli \
  -m Nanbeige4.2-3B-Q4_K_M.gguf \
  -c 4096 -n 256 \
  -p "Hello"

Stock llama.cpp builds that do not include Nanbeige support will reject the nanbeige architecture. The patch adds conversion metadata, tensor loading, loop-boundary normalization, and 44-layer cache handling; it does not replace model weights.

Validation

Built and tested on Windows with llama.cpp cf512566dc89d7a2bdefd43f6d4b85a1c18e93a7 and an AMD Ryzen 9 7950X3D.

Both files loaded as architecture nanbeige, block count 44, loop count 2.
Q4_K_M: prompt benchmark 92.77 tok/s; generation benchmark 15.67 tok/s.
Q8_0: prompt benchmark 53.95 tok/s; generation benchmark 9.37 tok/s.
Deterministic eight-token generation completed for both files.
Interactive Q8_0 sample measured 68.6 tok/s prompt and 10.2 tok/s generation.

These are CPU smoke/throughput tests, not quality benchmarks. The original model advertises a 262,144-token context, but local validation used a short 512-token context.

License

The base model is Apache-2.0 licensed. Review the upstream model card for its full terms and intended-use information.

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GGUF

Model size

4B params

Architecture

nanbeige

Hardware compatibility

4-bit

8-bit

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