LocoOperator — Development skill for Claude Code
Local 4B codebase explorer agent distilled from Qwen3-Coder-Next.
How to install LocoOperator
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open LocoreMind/LocoOperator and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
What LocoOperator does
Local 4B codebase explorer agent distilled from Qwen3-Coder-Next.
Alternatives in Development
- Happy Coder — by GrocerPublishAgent - Spawn and control multiple Claude Codes in parallel from your phone or desktop 15.7k ★
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- Code Review Graph — Local knowledge graph for Claude Code 3.7k ★
README
[](https://huggingface.co/LocoreMind/LocoOperator-4B) [](https://huggingface.co/LocoreMind/LocoOperator-4B-GGUF) [](https://locoremind.com/blog/loco-operator) [](https://github.com/LocoreMind/LocoOperator) [](https://colab.research.google.com/github/LocoreMind/LocoOperator/blob/main/LocoOperator_4B.ipynb) [](https://modelscope.cn/organization/LocoreMind)
**LocoOperator-4B** is a 4B-parameter code exploration agent distilled from Qwen3-Coder-Next. Designed as a local sub agent for Claude Code-style agent loops — fast codebase navigation at zero API cost.
📋 Table of Contents
- 📰 News & Updates
- 📝 Introduction
- ✨ Key Features
- 🏗️ Architecture
- 📈 Performance
- 🚀 Quick Start
- 🔧 Analysis Pipeline
- 📁 Project Structure
- ⚠️ Known Limitations
- 📄 License
- 🙏 Acknowledgments
📰 News & Updates
- [2026-02-24] 🚀 LocoOperator-4B-GGUF released for local deployment.
- [2026-02-23] 🎉 LocoOperator-4B model card and evaluation analysis released.
📝 Introduction
LocoOperator-4B is a tool-calling agent model trained via knowledge distillation from **Qwen3-Coder-Next** inference traces. It specializes in multi-turn codebase exploration — reading files, searching code, and navigating project structures within a Claude Code-style agent loop.
| LocoOperator-4B | |
|---|---|
| Base Model | Qwen3-4B-Instruct-2507 |
| Teacher Model | Qwen3-Coder-Next |
| Training Method | Full-parameter SFT (distillation) |
| Training Data | 170,356 multi-turn conversation samples |
| Max Sequence Length | 16,384 tokens |
| Training Hardware | 4x NVIDIA H200 141GB SXM5 |
| Training Time | ~25 hours |
| Framework | MS-SWIFT |
✨ Key Features
- 🔧 Tool-Calling Agent: Generates structured `<tool_call
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