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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="WaveCut/Nanbeige4.2-3B-heretic-GGUF",
	filename="Nanbeige4.2-3B-heretic-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 WaveCut/Nanbeige4.2-3B-heretic-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 WaveCut/Nanbeige4.2-3B-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf WaveCut/Nanbeige4.2-3B-heretic-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 WaveCut/Nanbeige4.2-3B-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf WaveCut/Nanbeige4.2-3B-heretic-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 WaveCut/Nanbeige4.2-3B-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf WaveCut/Nanbeige4.2-3B-heretic-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 WaveCut/Nanbeige4.2-3B-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WaveCut/Nanbeige4.2-3B-heretic-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use WaveCut/Nanbeige4.2-3B-heretic-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

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

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

Unsloth Studio

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

Using HuggingFace Spaces for Unsloth

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

How to use WaveCut/Nanbeige4.2-3B-heretic-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WaveCut/Nanbeige4.2-3B-heretic-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": "WaveCut/Nanbeige4.2-3B-heretic-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use WaveCut/Nanbeige4.2-3B-heretic-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WaveCut/Nanbeige4.2-3B-heretic-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 "WaveCut/Nanbeige4.2-3B-heretic-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 WaveCut/Nanbeige4.2-3B-heretic-GGUF with Docker Model Runner:
```
docker model run hf.co/WaveCut/Nanbeige4.2-3B-heretic-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Nanbeige4.2-3B Heretic GGUF

Selected 4-bit-and-up GGUF quantizations of WaveCut/Nanbeige4.2-3B-heretic.

File	Role
`Nanbeige4.2-3B-heretic-Q4_K_M.gguf`	Recommended compact mixed-precision default
`Nanbeige4.2-3B-heretic-Q5_K_M.gguf`	Recommended quality/speed balance
`Nanbeige4.2-3B-heretic-Q6_K.gguf`	High-quality K-quant
`Nanbeige4.2-3B-heretic-Q8_0.gguf`	Near-lossless quality anchor

No legacy Q4_0, Q5_0, or redundant same-bit variants are included.

The K-quants were calibrated with an importance matrix built from a deterministically shuffled agentic/coding corpus. Sources and revisions:

TIGER-Lab/SWE-QA-Pro-SFT-Trajectories at b8f5b8a8dcf90bca8b6d70adedac0d20dca02b86.
nvidia/OpenCodeReasoning at 20a1ca19c0d050fe9057fc08339d6b370ec1c67a.

Corpus SHA-256: a7cfdbe02c124304bf1282bbd5ed7162bfa72dec6750b60ed2d3a68000c7a554. The imatrix input takes 256 evenly spaced corpus records, truncates each to 1,024 tokens, and processes 256 context-1,024 input chunks with special-token parsing. Nanbeige's two execution loops produce 512 internal imatrix passes. The derived text SHA-256 is de99993785d460f0c48a8c35d36b764ef217ee227d6354cbeae0dc2cb155a30e.

Compatibility

Nanbeige 4.2 is a looped Transformer: 22 physical layers are executed twice. These files were converted and validated with Nanbeige's llama.cpp branch at revision 26cfdc4409cfc67d27be9b71c9de79adaf5f306f. Use that revision or a newer llama.cpp build containing equivalent Nanbeige support.

./llama-cli \
  -m Nanbeige4.2-3B-heretic-Q5_K_M.gguf \
  -cnv -p "Write a robust retry helper in Python."

Exact file sizes, SHA-256 hashes, imatrix settings, and smoke-test throughput are recorded in release-manifest.json.

Downloads last month: 2,029

GGUF

Model size

4B params

Architecture

nanbeige

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Model tree for WaveCut/Nanbeige4.2-3B-heretic-GGUF

Base model

Nanbeige/Nanbeige4.2-3B-Base

Finetuned

Nanbeige/Nanbeige4.2-3B

Finetuned

WaveCut/Nanbeige4.2-3B-heretic

Quantized

(2)

this model