LocoOperator-4B — MLX 4-bit Quantized

This is a 4-bit quantized MLX version of LocoreMind/LocoOperator-4B, converted for efficient inference on Apple Silicon using MLX.

Model Overview

About LocoOperator-4B

LocoOperator-4B is a 4B-parameter 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.

Key Features

• Tool-Calling Agent: Generates structured <tool_call> JSON for Read, Grep, Glob, Bash, Write, Edit, and Task (subagent delegation)
• 100% JSON Validity: Every tool call is valid JSON with all required arguments — outperforming the teacher model (87.6%)
• Multi-Turn: Handles conversation depths of 3–33 messages with consistent tool-calling behavior

Performance

Usage with MLX

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("DJLougen/LocoOperator-4B-MLX-4bit")

messages = [
    {
        "role": "system",
        "content": "You are a read-only codebase search specialist."
    },
    {
        "role": "user",
        "content": "Analyze the project structure at /workspace/myproject and explain the architecture."
    }
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
response = generate(model, tokenizer, prompt=prompt, max_tokens=512)
print(response)

Other Quantizations

Acknowledgments

• LocoreMind for the original LocoOperator-4B model
• Qwen Team for the Qwen3-4B-Instruct-2507 base model
• Apple MLX Team for the MLX framework