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LocoOperator-4B-GGUF

Name: LocoOperator-4B-GGUF
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gguf

text-generation

LocoreMind/LocoOperator-4B

quantized

endpoints_compatible

conversational

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Quantized at Romarchive

Introduction

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. Designed as a local sub agent, it runs via llama.cpp at zero API cost.

	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> 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%)
Local Deployment: GGUF quantized, runs on Mac Studio via llama.cpp at zero API cost
Lightweight Explorer: 4B parameters, optimized for fast codebase search and navigation
Multi-Turn: Handles conversation depths of 3–33 messages with consistent tool-calling behavior

Performance

Evaluated on 65 multi-turn conversation samples from diverse open-source projects (scipy, fastapi, arrow, attrs, gevent, gunicorn, etc.), with labels generated by Qwen3-Coder-Next.

Core Metrics

Metric	Score
Tool Call Presence Alignment	100% (65/65)
First Tool Type Match	65.6% (40/61)
JSON Validity	100% (76/76)
Argument Syntax Correctness	100% (76/76)

The model perfectly learned when to use tools vs. when to respond with text (100% presence alignment). Tool type mismatches are between semantically similar tools (e.g. Grep vs Read) — different but often valid strategies.

Tool Distribution Comparison

JSON & Argument Syntax Correctness

Model	JSON Valid	Argument Syntax Valid
LocoOperator-4B	76/76 (100%)	76/76 (100%)
Qwen3-Coder-Next (teacher)	89/89 (100%)	78/89 (87.6%)

LocoOperator-4B achieves perfect structured output. The teacher model has 11 tool calls with missing required arguments (empty arguments: {}).

Quick Start

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "LocoreMind/LocoOperator-4B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the messages
messages = [
    {
        "role": "system",
        "content": "You are a read-only codebase search specialist.\n\nCRITICAL CONSTRAINTS:\n1. STRICTLY READ-ONLY: You cannot create, edit, delete, move files, or run any state-changing commands. Use tools/bash ONLY for reading (e.g., ls, find, cat, grep).\n2. EFFICIENCY: Spawn multiple parallel tool calls for faster searching.\n3. OUTPUT RULES: \n   - ALWAYS use absolute file paths.\n   - STRICTLY NO EMOJIS in your response.\n   - Output your final report directly. Do not use colons before tool calls.\n\nENV: Working directory is /Users/developer/workspace/code-analyzer (macOS, zsh)."
    },
    {
        "role": "user",
        "content": "Analyze the Black codebase at `/Users/developer/workspace/code-analyzer/projects/black`.\nFind and explain:\n1. How Black discovers config files.\n2. The exact search order for config files.\n3. Supported config file formats.\n4. Where this configuration discovery logic lives in the codebase.\n\nReturn a comprehensive answer with relevant code snippets and absolute file paths."
    }
]

# prepare the model input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512,
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

content = tokenizer.decode(output_ids, skip_special_tokens=True)
print(content)

Local Deployment

For GGUF quantized deployment with llama.cpp, hybrid proxy routing, and batch analysis pipelines, refer to our GitHub repository.

Training Details

Parameter	Value
Base model	Qwen3-4B-Instruct-2507
Teacher model	Qwen3-Coder-Next
Method	Full-parameter SFT
Training data	170,356 samples
Hardware	4x NVIDIA H200 141GB SXM5
Parallelism	DDP (no DeepSpeed)
Precision	BF16
Epochs	1
Batch size	2/GPU, gradient accumulation 4 (effective batch 32)
Learning rate	2e-5, warmup ratio 0.03
Max sequence length	16,384 tokens
Template	qwen3_nothinking
Framework	MS-SWIFT
Training time	~25 hours
Checkpoint	Step 2524

Known Limitations

First-tool-type match is 65.6% — the model sometimes picks a different (but not necessarily wrong) tool than the teacher
Tends to under-generate parallel tool calls compared to the teacher (76 vs 89 total calls across 65 samples)
Preference for Bash over Read may indicate the model defaults to shell commands where file reads would be more appropriate
Evaluated on 65 samples only; larger-scale evaluation needed

License

MIT

Acknowledgments

Qwen Team for the Qwen3-4B-Instruct-2507 base model
MS-SWIFT for the training framework
llama.cpp for efficient local inference
Anthropic for the Claude Code agent loop design that inspired this work

Romarchive/LocoOperator-4B-GGUF