Instructions to use LocoreMind/LocoOperator-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LocoreMind/LocoOperator-4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LocoreMind/LocoOperator-4B-GGUF",
	filename="LocoOperator-4B.IQ4_XS.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use LocoreMind/LocoOperator-4B-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 LocoreMind/LocoOperator-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LocoreMind/LocoOperator-4B-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 LocoreMind/LocoOperator-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LocoreMind/LocoOperator-4B-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 LocoreMind/LocoOperator-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LocoreMind/LocoOperator-4B-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 LocoreMind/LocoOperator-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LocoreMind/LocoOperator-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/LocoreMind/LocoOperator-4B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use LocoreMind/LocoOperator-4B-GGUF with Ollama:
```
ollama run hf.co/LocoreMind/LocoOperator-4B-GGUF:Q4_K_M
```

Unsloth Studio

How to use LocoreMind/LocoOperator-4B-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 LocoreMind/LocoOperator-4B-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 LocoreMind/LocoOperator-4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LocoreMind/LocoOperator-4B-GGUF to start chatting

How to use LocoreMind/LocoOperator-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LocoreMind/LocoOperator-4B-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": "LocoreMind/LocoOperator-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use LocoreMind/LocoOperator-4B-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 LocoreMind/LocoOperator-4B-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 LocoreMind/LocoOperator-4B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use LocoreMind/LocoOperator-4B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LocoreMind/LocoOperator-4B-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 "LocoreMind/LocoOperator-4B-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 LocoreMind/LocoOperator-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/LocoreMind/LocoOperator-4B-GGUF:Q4_K_M
```

Lemonade

How to use LocoreMind/LocoOperator-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LocoreMind/LocoOperator-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LocoOperator-4B-GGUF-Q4_K_M

List all available models

lemonade list

LocoOperator-4B-GGUF / README.md

FutureMa

Create README.md

a84d7d0 verified 5 months ago

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	---
	license: mit
	base_model: LocoreMind/LocoOperator-4B
	tags:
	- code
	- agent
	- tool-calling
	- gguf
	- llama-cpp
	- qwen
	---

	# LocoOperator-4B-GGUF

	This repository contains the official GGUF quantized versions of [LocoOperator-4B](https://huggingface.co/LocoreMind/LocoOperator-4B).

	LocoOperator-4B is a 4B-parameter code exploration agent distilled from Qwen3-Coder-Next. It is specifically optimized for local agent loops (like Claude Code style), providing high-speed codebase navigation with 100% JSON tool-calling validity.

	## 🚀 Which file should I choose?

	We provide several quantization levels to balance performance and memory usage:

	\| File Name \| Size \| Recommendation \|
	\|-----------\|------\|----------------\|
	\| LocoOperator-4B.Q8_0.gguf \| 4.28 GB \| Best Accuracy. Recommended for local agent loops to ensure perfect JSON output. \|
	\| LocoOperator-4B.Q6_K.gguf \| 3.31 GB \| Great Balance. Near-lossless logic with a smaller footprint. \|
	\| LocoOperator-4B.Q4_K_M.gguf\| 2.50 GB \| Standard. Compatible with almost all local LLM runners (LM Studio, Ollama, etc.). \|
	\| LocoOperator-4B.IQ4_XS.gguf\| 2.29 GB \| Advanced. Uses Importance Quantization for better performance at smaller sizes. \|

	## 🛠 Usage (llama.cpp)

	To run this model using `llama-cli` or `llama-server`, we recommend a context size of at least 50K to handle multi-turn codebase exploration:

	### Simple CLI Chat:
	```bash
	./llama-cli \
	-m LocoOperator-4B.Q8_0.gguf \
	-c 51200 \
	-p "You are a helpful codebase explorer. Use tools to help the user."
	```

	### Serve as an OpenAI-compatible API:
	```bash
	./llama-server \
	-m LocoOperator-4B.Q8_0.gguf \
	--ctx-size 51200 \
	--port 8080
	```

	## 📋 Model Details
	- Base Model: Qwen3-4B-Instruct-2507
	- Teacher Model: Qwen3-Coder-Next
	- Training Method: Full-parameter SFT (Knowledge Distillation)
	- Primary Use Case: Codebase exploration (Read, Grep, Glob, Bash, Task)

	## 🔗 Links
	- Main Repository: [LocoreMind/LocoOperator-4B](https://huggingface.co/LocoreMind/LocoOperator-4B)
	- GitHub: [LocoreMind/LocoOperator](https://github.com/LocoreMind/LocoOperator)
	- Blog: [locoremind.com/blog/loco-operator](https://locoremind.com/blog/loco-operator)

	## 🙏 Acknowledgments
	Special thanks to `mradermacher` for the initial quantization work and the `llama.cpp` community.