Instructions to use Flexan/LocoreMind-LocoOperator-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Flexan/LocoreMind-LocoOperator-4B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Flexan/LocoreMind-LocoOperator-4B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Flexan/LocoreMind-LocoOperator-4B-GGUF", dtype="auto")

llama-cpp-python

How to use Flexan/LocoreMind-LocoOperator-4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Flexan/LocoreMind-LocoOperator-4B-GGUF",
	filename="LocoOperator-4B.IQ3_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Flexan/LocoreMind-LocoOperator-4B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Flexan/LocoreMind-LocoOperator-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Flexan/LocoreMind-LocoOperator-4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Flexan/LocoreMind-LocoOperator-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Flexan/LocoreMind-LocoOperator-4B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Flexan/LocoreMind-LocoOperator-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Flexan/LocoreMind-LocoOperator-4B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Flexan/LocoreMind-LocoOperator-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Flexan/LocoreMind-LocoOperator-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Flexan/LocoreMind-LocoOperator-4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Flexan/LocoreMind-LocoOperator-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Flexan/LocoreMind-LocoOperator-4B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Flexan/LocoreMind-LocoOperator-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Flexan/LocoreMind-LocoOperator-4B-GGUF:Q4_K_M

SGLang

How to use Flexan/LocoreMind-LocoOperator-4B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Flexan/LocoreMind-LocoOperator-4B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Flexan/LocoreMind-LocoOperator-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Flexan/LocoreMind-LocoOperator-4B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Flexan/LocoreMind-LocoOperator-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Flexan/LocoreMind-LocoOperator-4B-GGUF with Ollama:
```
ollama run hf.co/Flexan/LocoreMind-LocoOperator-4B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Flexan/LocoreMind-LocoOperator-4B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Flexan/LocoreMind-LocoOperator-4B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Flexan/LocoreMind-LocoOperator-4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Flexan/LocoreMind-LocoOperator-4B-GGUF to start chatting

Pi new

How to use Flexan/LocoreMind-LocoOperator-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Flexan/LocoreMind-LocoOperator-4B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Flexan/LocoreMind-LocoOperator-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Flexan/LocoreMind-LocoOperator-4B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Flexan/LocoreMind-LocoOperator-4B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Flexan/LocoreMind-LocoOperator-4B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Flexan/LocoreMind-LocoOperator-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/Flexan/LocoreMind-LocoOperator-4B-GGUF:Q4_K_M
```

Lemonade

How to use Flexan/LocoreMind-LocoOperator-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Flexan/LocoreMind-LocoOperator-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LocoreMind-LocoOperator-4B-GGUF-Q4_K_M

List all available models

lemonade list

GGUF Files for LocoOperator-4B

These are the GGUF files for LocoreMind/LocoOperator-4B.

Downloads

GGUF Link	Quantization	Description
Download	Q2_K	Lowest quality
Download	Q3_K_S
Download	IQ3_S	Integer quant, preferable over Q3_K_S
Download	IQ3_M	Integer quant
Download	Q3_K_M
Download	Q3_K_L
Download	IQ4_XS	Integer quant
Download	Q4_K_S	Fast with good performance
Download	Q4_K_M	Recommended: Perfect mix of speed and performance
Download	Q5_K_S
Download	Q5_K_M
Download	Q6_K	Very good quality
Download	Q8_0	Best quality
Download	f16	Full precision, don't bother; use a quant

Note from Flexan

I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet. This process is not yet automated and I download, convert, quantize, and upload them by hand, usually for models I deem interesting and wish to try out.

If there are some quants missing that you'd like me to add, you may request one in the community tab. If you want to request a public model to be converted, you can also request that in the community tab. If you have questions regarding the model, please refer to the original model repo.

Model Card for LocoOperator-4B

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

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

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GGUF

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Architecture

qwen3

Hardware compatibility

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Model tree for Flexan/LocoreMind-LocoOperator-4B-GGUF

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

LocoreMind/LocoOperator-4B

Quantized

(18)

this model

Collection including Flexan/LocoreMind-LocoOperator-4B-GGUF

Community GGUFs

Collection

This collection contains quantized GGUF files for community models that did not have GGUF equivalents available yet. I do not own these models. • 58 items • Updated Apr 16