Instructions to use Kelnux/Qwen3-0.6B-seo-finetuned-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kelnux/Qwen3-0.6B-seo-finetuned-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Kelnux/Qwen3-0.6B-seo-finetuned-GGUF",
	filename="Qwen3-0.6B-seo-finetuned-f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Kelnux/Qwen3-0.6B-seo-finetuned-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Kelnux/Qwen3-0.6B-seo-finetuned-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Kelnux/Qwen3-0.6B-seo-finetuned-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Kelnux/Qwen3-0.6B-seo-finetuned-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Kelnux/Qwen3-0.6B-seo-finetuned-GGUF:F16

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 Kelnux/Qwen3-0.6B-seo-finetuned-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Kelnux/Qwen3-0.6B-seo-finetuned-GGUF:F16

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 Kelnux/Qwen3-0.6B-seo-finetuned-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Kelnux/Qwen3-0.6B-seo-finetuned-GGUF:F16

Use Docker

docker model run hf.co/Kelnux/Qwen3-0.6B-seo-finetuned-GGUF:F16

LM Studio
Jan

vLLM

How to use Kelnux/Qwen3-0.6B-seo-finetuned-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kelnux/Qwen3-0.6B-seo-finetuned-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": "Kelnux/Qwen3-0.6B-seo-finetuned-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kelnux/Qwen3-0.6B-seo-finetuned-GGUF:F16

Ollama
How to use Kelnux/Qwen3-0.6B-seo-finetuned-GGUF with Ollama:
```
ollama run hf.co/Kelnux/Qwen3-0.6B-seo-finetuned-GGUF:F16
```

Unsloth Studio

How to use Kelnux/Qwen3-0.6B-seo-finetuned-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 Kelnux/Qwen3-0.6B-seo-finetuned-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 Kelnux/Qwen3-0.6B-seo-finetuned-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Kelnux/Qwen3-0.6B-seo-finetuned-GGUF to start chatting

How to use Kelnux/Qwen3-0.6B-seo-finetuned-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Kelnux/Qwen3-0.6B-seo-finetuned-GGUF:F16

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": "Kelnux/Qwen3-0.6B-seo-finetuned-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Kelnux/Qwen3-0.6B-seo-finetuned-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 Kelnux/Qwen3-0.6B-seo-finetuned-GGUF:F16

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 Kelnux/Qwen3-0.6B-seo-finetuned-GGUF:F16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Kelnux/Qwen3-0.6B-seo-finetuned-GGUF with Docker Model Runner:
```
docker model run hf.co/Kelnux/Qwen3-0.6B-seo-finetuned-GGUF:F16
```

Lemonade

How to use Kelnux/Qwen3-0.6B-seo-finetuned-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Kelnux/Qwen3-0.6B-seo-finetuned-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3-0.6B-seo-finetuned-GGUF-F16

List all available models

lemonade list

Qwen3-0.6B SEO Fine-tuned (GGUF)

GGUF versions of Kelnux/Qwen3-0.6B-seo-finetuned for use with llama.cpp, Ollama, LM Studio, etc.

Available Files

File	Quantization	Size
Qwen3-0.6B-seo-finetuned-f16.gguf	F16 (full precision)	~1.1 GB
Qwen3-0.6B-seo-finetuned-q8_0.gguf	Q8_0 (8-bit quantized)	~610 MB

Training Details

Base model: Qwen/Qwen3-0.6B
Method: LoRA (r=16, alpha=32)
Dataset: metehan777/global-seo-knowledge (2,065 examples)
Final loss: 1.14
Token accuracy: 79.8%

Usage

With llama.cpp

llama-cli -m Qwen3-0.6B-seo-finetuned-q8_0.gguf -p "What is robots.txt in SEO?" -n 200

With Ollama

FROM ./Qwen3-0.6B-seo-finetuned-q8_0.gguf

With LM Studio

Download the GGUF file and load it directly in LM Studio.

Downloads last month: 17

GGUF

Model size

0.6B params

Architecture

qwen3

Hardware compatibility

8-bit

16-bit

Model tree for Kelnux/Qwen3-0.6B-seo-finetuned-GGUF

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Quantized

(329)

this model

Kelnux
/

Qwen3-0.6B-seo-finetuned-GGUF