---
title: LocoOperator-4B
canonical_url: "https://www.modelscope.cn/models/LocoreMind/LocoOperator-4B"
md_url: "https://www.modelscope.cn/models/LocoreMind/LocoOperator-4B.md"
repository: LocoreMind/LocoOperator-4B
last_updated: 2026-02-25
license: mit
pipeline_tag: text-generation
tasks:
- text-generation
model_type:
- qwen3
architectures:
- Qwen3ForCausalLM
base_model:
- Qwen/Qwen3-4B-Instruct-2507
base_model_relation: finetune
parameters: 4.0B
tensor_type:
- BF16
library_name:
- transformer
- safetensors
- pytorch
frameworks:
- pytorch
language:
- en
inference_backends:
- "deploy_task text/emb"
- "lmdeploy_turbomind 0.9.1"
- "sglang 0.5.2"
- "vllm 0.9.2"
downloads: 4305
stars: 14
tags:
- code
- agent
- tool-calling
- distillation
- qwen3
- gguf
- llama-cpp
---
# LocoOperator-4B
> LocoOperator-4B - LocoreMind 在 ModelScope 开源的模型。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…
LocoreMind/LocoOperator-4B 是 ModelScope 魔搭社区上的 4.0B 参数text-generation模型，采用 mit 许可，基于 Qwen/Qwen3-4B-Instruct-2507 构建，可用 deploy_task text/emb、lmdeploy_turbomind 0.9.1、sglang 0.5.2 部署。
- **Repository**: LocoreMind/LocoOperator-4B
- **License**: mit
- **Tasks**: text-generation
- **Parameters**: 4.0B
- **Base model**: Qwen/Qwen3-4B-Instruct-2507
- **Inference backends**: deploy_task text/emb, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: code, agent, tool-calling, distillation, qwen3, gguf, llama-cpp
- **Downloads**: 4305
- **Stars**: 14
- **Last updated**: 2026-02-25
Source: https://www.modelscope.cn/models/LocoreMind/LocoOperator-4B
---
[](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)
## 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](https://huggingface.co/Qwen/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 `` 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](https://github.com/LocoreMind/LocoOperator).
## 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](https://huggingface.co/Qwen) for the Qwen3-4B-Instruct-2507 base model
- [MS-SWIFT](https://github.com/modelscope/ms-swift) for the training framework
- [llama.cpp](https://github.com/ggerganov/llama.cpp) for efficient local inference
- [Anthropic](https://www.anthropic.com/) for the Claude Code agent loop design that inspired this work
