Instructions to use fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K

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 fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K

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 fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K

Use Docker

docker model run hf.co/fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K

LM Studio
Jan
Ollama
How to use fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF with Ollama:
```
ollama run hf.co/fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K
```
Unsloth Desktop

How to use fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF with Docker Model Runner:
```
docker model run hf.co/fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K
```

Lemonade

How to use fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K

Run and chat with the model

lemonade run user.qwen3-4b-instruct-2507-sys-prompter-GGUF-Q6_K

List all available models

lemonade list

Hermes Agent

How to use fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K

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 fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF:Q6_K" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3-4B-Instruct-System-Prompter

This model is a fine-tuned version of unsloth/Qwen3-4B-Instruct-2507, specialized in generating detailed and creative system prompts from short user descriptions.

It was fine-tuned using Unsloth and exported to GGUF format for efficient local inference.

Model Description

Model Type: Qwen2/Qwen3
Language(s): English
License: Apache 2.0
Finetuned from model: unsloth/Qwen3-4B-Instruct-2507

Use Cases

This model is designed to act as a meta-prompter. You give it a high-level persona or task description, and it generates a comprehensive system prompt that you can use to configure another LLM.

How to Use

This repo contains GGUF quantized models:

*Q6_K.gguf
*Q8_0.gguf

Dataset

The model was trained on finetune_dataset.json, which contains examples of user requests and corresponding detailed system prompts.

Downloads last month: 23

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

6-bit

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for fuutott/qwen3-4b-instruct-2507-sys-prompter-GGUF

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

unsloth/Qwen3-4B-Instruct-2507

Quantized

(32)

this model