Instructions to use QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF",
	filename="Qwen2.5-7B-Gutenberg-KTO.Q2_K.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 QuantFactory/Qwen2.5-7B-Gutenberg-KTO-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 QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Qwen2.5-7B-Gutenberg-KTO-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 QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Qwen2.5-7B-Gutenberg-KTO-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 QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Qwen2.5-7B-Gutenberg-KTO-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 QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Qwen2.5-7B-Gutenberg-KTO-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": "QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Qwen2.5-7B-Gutenberg-KTO-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 QuantFactory/Qwen2.5-7B-Gutenberg-KTO-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 QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF to start chatting

How to use QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Qwen2.5-7B-Gutenberg-KTO-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": "QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/Qwen2.5-7B-Gutenberg-KTO-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 QuantFactory/Qwen2.5-7B-Gutenberg-KTO-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 QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Qwen2.5-7B-Gutenberg-KTO-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 "QuantFactory/Qwen2.5-7B-Gutenberg-KTO-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"

Docker Model Runner
How to use QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-7B-Gutenberg-KTO-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF

This is quantized version of Orion-zhen/Qwen2.5-7B-Gutenberg-KTO created using llama.cpp

Original Model Card

Qwen2.5-7B-Gutenberg-KTO

This model is fine tuned over gutenberg datasets using kto strategy. It's my first time to use kto strategy, and I'm not sure how the model actually performs.

Compared to those large companies which remove accessories such as charger and cables from packages, I have achieved real environment protection by truly reducing energy consumption, rather than shifting costs to consumers.

Checkout GGUF here: Orion-zhen/Qwen2.5-7B-Gutenberg-KTO-Q6_K-GGUF

Details

Platform

~~I randomly grabbed some rubbish from a second-hand market and built a PC~~

I carefully selected various dedicated hardwares and constructed an incomparable home server, which I entitled the Great Server:

CPU: Intel Core i3-4160
Memory: 8G DDR3, single channel
GPU: Tesla P4, TDP 75W, boasting its Eco friendly energy consumption
Disk: 1TB M.2 NVME, PCIe 4.0

Training

To practice the eco-friendly training, I utilized various methods, including adam-mini, qlora and unsloth, to minimize VRAM and energy usage, as well as accelerating training speed.

dataset: Orion-zhen/kto-gutenberg
epoch: 2
gradient accumulation: 8
batch size: 1
KTO perf beta: 0.1

Train log

Downloads last month: 204

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

Orion-zhen/Qwen2.5-7B-Instruct-Uncensored

Quantized

(14)

this model

QuantFactory
/

Qwen2.5-7B-Gutenberg-KTO-GGUF

QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF

Original Model Card

Qwen2.5-7B-Gutenberg-KTO

Details

Platform

Training

Train log

Model tree for QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF

Dataset used to train QuantFactory/Qwen2.5-7B-Gutenberg-KTO-GGUF