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

Libraries

How to use QuantFactory/Qwen2.5-7B-Instruct-Uncensored-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-Instruct-Uncensored-GGUF",
	filename="Qwen2.5-7B-Instruct-Uncensored.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-Instruct-Uncensored-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Qwen2.5-7B-Instruct-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Qwen2.5-7B-Instruct-Uncensored-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 QuantFactory/Qwen2.5-7B-Instruct-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Qwen2.5-7B-Instruct-Uncensored-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-Instruct-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Qwen2.5-7B-Instruct-Uncensored-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-Instruct-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Qwen2.5-7B-Instruct-Uncensored-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use QuantFactory/Qwen2.5-7B-Instruct-Uncensored-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-Instruct-Uncensored-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-Instruct-Uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio

How to use QuantFactory/Qwen2.5-7B-Instruct-Uncensored-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-Instruct-Uncensored-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-Instruct-Uncensored-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-Instruct-Uncensored-GGUF to start chatting

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

Lemonade

How to use QuantFactory/Qwen2.5-7B-Instruct-Uncensored-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.Qwen2.5-7B-Instruct-Uncensored-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Qwen2.5-7B-Instruct-Uncensored-GGUF

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

Original Model Card

Qwen2.5-7B-Instruct-Uncensored

This model is an uncensored fine-tune version of Qwen2.5-7B-Instruct. However, I can still notice that though uncensored, the model fails to generate detailed descriptions on certain extreme scenarios, which might be associated with deletion on some pretrain datasets in Qwen's pretraining stage.

Check out my roleplay&writing enhanced model based on this model: Orion-zhen/Meissa-Qwen2.5-7B-Instruct

Traning details

I used SFT + DPO to ensure uncensorment as well as trying to maintain original model's capabilities.

SFT:
- NobodyExistsOnTheInternet/ToxicQAFinal
- anthracite-org/kalo-opus-instruct-22k-no-refusal
DPO:
- Orion-zhen/dpo-toxic-zh
- unalignment/toxic-dpo-v0.2
- Crystalcareai/Intel-DPO-Pairs-Norefusals

Downloads last month: 15,113

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-Instruct-Uncensored-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(315)

this model

QuantFactory
/

Qwen2.5-7B-Instruct-Uncensored-GGUF

QuantFactory/Qwen2.5-7B-Instruct-Uncensored-GGUF

Original Model Card

Qwen2.5-7B-Instruct-Uncensored

Traning details

Model tree for QuantFactory/Qwen2.5-7B-Instruct-Uncensored-GGUF

Datasets used to train QuantFactory/Qwen2.5-7B-Instruct-Uncensored-GGUF