Instructions to use tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-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 tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-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 tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-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 tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-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 tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF:Q4_K_M

Use Docker

docker model run hf.co/tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-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": "tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF:Q4_K_M

Ollama
How to use tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF with Ollama:
```
ollama run hf.co/tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF:Q4_K_M
```
Unsloth Desktop

How to use tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF:Q4_K_M

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": "tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF with Docker Model Runner:
```
docker model run hf.co/tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF:Q4_K_M
```

Lemonade

How to use tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-4B-Empero-AI-FullStack-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-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 tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-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 tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF:Q4_K_M

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 "tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF:Q4_K_M" \
  --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"

Qwen3.8-4B-Empero-AI-FullStack — GGUF Quantizations

This repository contains GGUF quantizations of iBotIA/Qwen3.8-4B-Empero-AI-FullStack.

📦 Available Quantizations

File	Bits	Size (approx.)	Use case
`model_f16.gguf`	16-bit	~8.7 GB	Maximum quality, reference
`model_q8_0.gguf`	8-bit	~4.7 GB	Near-lossless, high VRAM
`model_q6_k.gguf`	6-bit	~3.6 GB	Excellent quality
`model_q5_k_m.gguf`	5-bit	~3.1 GB	Great quality/size balance
`model_q5_k_s.gguf`	5-bit	~3.0 GB	Slightly smaller than K_M
`model_q4_k_m.gguf`	4-bit	~2.5 GB	Recommended default
`model_q4_k_s.gguf`	4-bit	~2.4 GB	Smaller 4-bit variant
`model_q3_k_l.gguf`	3-bit	~2.1 GB	Low VRAM, decent quality
`model_q3_k_m.gguf`	3-bit	~1.9 GB	Balanced 3-bit
`model_q3_k_s.gguf`	3-bit	~1.7 GB	Minimum 3-bit
`model_q2_k.gguf`	2-bit	~1.3 GB	Extreme compression

IQ quants (IQ4_XS, etc.) coming soon — require imatrix calibration.

🚀 Usage

llama.cpp

./llama-cli -m model_q4_k_m.gguf -p "Your prompt here" -n 512

LM Studio

Download any .gguf file and load it directly in LM Studio.

Ollama

ollama run hf.co/tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF:Q4_K_M

Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="tinyopsec/Qwen3.8-4B-Empero-AI-FullStack-GGUF",
    filename="model_q4_k_m.gguf",
)
output = llm("Your prompt here", max_tokens=512)
print(output["choices"][0]["text"])