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+LocoOperator[[:space:]]4B[[:space:]]IQ3_M[[:space:]](2026)\[Romarchive\].gguf.gguf filter=lfs diff=lfs merge=lfs -text
+LocoOperator[[:space:]]4B[[:space:]]Q4_0[[:space:]](2026)\[Romarchive\].gguf.gguf filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+library_name: gguf
+pipeline_tag: text-generation
+base_model:
+- LocoreMind/LocoOperator-4B
+---
+Quantized at [Romarchive](https://cows.info.gf)
+
+
+<div align="center">
+  <img src="assets/loco_operator.png" width="55%" alt="LocoOperator" />
+</div>
+
+<br>
+
+<div align="center">
+
+[![MODEL](https://img.shields.io/badge/Model-FFB300?style=for-the-badge&logo=huggingface&logoColor=white)](https://huggingface.co/LocoreMind/LocoOperator-4B)
+[![GGUF](https://img.shields.io/badge/GGUF-FF6F00?style=for-the-badge&logo=huggingface&logoColor=white)](https://huggingface.co/LocoreMind/LocoOperator-4B-GGUF)
+[![Blog](https://img.shields.io/badge/Blog-4285F4?style=for-the-badge&logo=google-chrome&logoColor=white)](https://locoremind.com/blog/loco-operator)
+[![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github&logoColor=white)](https://github.com/LocoreMind/LocoOperator)
+[![Colab](https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&logoColor=white)](https://colab.research.google.com/github/LocoreMind/LocoOperator/blob/main/LocoOperator_4B.ipynb)
+
+</div>
+
+## 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 `<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
+
+<div align="center">
+  <img src="assets/tool_distribution.png" width="80%" alt="Tool Distribution Comparison" />
+</div>
+
+### 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