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

Libraries

How to use Andycurrent/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="Andycurrent/Qwen2.5-7B-Instruct-Uncensored_GGUF",
	filename="Qwen2.5-7B-Instruct-Uncensored_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 Andycurrent/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 Andycurrent/Qwen2.5-7B-Instruct-Uncensored_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Andycurrent/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 Andycurrent/Qwen2.5-7B-Instruct-Uncensored_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Andycurrent/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 Andycurrent/Qwen2.5-7B-Instruct-Uncensored_GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Andycurrent/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 Andycurrent/Qwen2.5-7B-Instruct-Uncensored_GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Andycurrent/Qwen2.5-7B-Instruct-Uncensored_GGUF:Q4_K_M

Use Docker

docker model run hf.co/Andycurrent/Qwen2.5-7B-Instruct-Uncensored_GGUF:Q4_K_M

LM Studio
Jan

vLLM

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

Use Docker

docker model run hf.co/Andycurrent/Qwen2.5-7B-Instruct-Uncensored_GGUF:Q4_K_M

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

Unsloth Studio

How to use Andycurrent/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 Andycurrent/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 Andycurrent/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 Andycurrent/Qwen2.5-7B-Instruct-Uncensored_GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use Andycurrent/Qwen2.5-7B-Instruct-Uncensored_GGUF with Docker Model Runner:
```
docker model run hf.co/Andycurrent/Qwen2.5-7B-Instruct-Uncensored_GGUF:Q4_K_M
```

Lemonade

How to use Andycurrent/Qwen2.5-7B-Instruct-Uncensored_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Andycurrent/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

Qwen2.5-7B-Instruct-Uncensored

Qwen2.5-7B-Instruct-Uncensored is a 7-billion-parameter instruction-following language model designed for open-ended interaction, research experimentation, and local deployment scenarios where users require minimal alignment constraints and maximal behavioral flexibility.

This model is intended for technically proficient users who want direct control over prompting, alignment, and downstream usage without heavy built-in moderation layers.

Model Summary

Model Name: Qwen2.5-7B-Instruct-Uncensored
Base Architecture: Qwen2.5-7B
Maintainer: Orion-zhen
Parameter Count: 7B
Model Type: Decoder-only transformer, instruction-tuned
License: Inherits the license terms of the original Qwen2.5 base model
Primary Focus: Open instruction following with reduced safety filtering

Design Philosophy

This release emphasizes instruction fidelity and conversational openness over restrictive alignment. The uncensored variant is designed to:

Respond directly to user instructions without excessive refusal patterns
Support experimentation with prompt engineering and alignment research
Enable private, offline, or air-gapped deployments
Serve as a flexible base for further fine-tuning or specialization

Instruction Format

For best results, interactions should follow a structured chat format compatible with Qwen-style instruction tuning:

<|system|>
Optional system-level guidance or role definition
<|user|>
User input or task description
<|assistant|>
Model response

Clear role separation improves consistency, especially in multi-turn conversations and complex reasoning tasks.

Core Capabilities

Strong adherence to explicit user instructions
Capable of multi-step reasoning and long-form responses
Performs well in coding, analysis, writing, and ideation tasks
Suitable for creative generation, simulations, and role-based interactions
Stable in extended dialogues without excessive context loss
Compatible with local inference stacks and quantized runtimes

Suggested Applications

Local AI assistants for private workflows
Research environments studying model behavior and alignment
Developer tooling such as code explanation and generation
Creative projects including storytelling and world-building
Prompt engineering experimentation
Offline or privacy-sensitive deployments

Responsible Usage Notice

This model intentionally minimizes automated content restrictions. Users are responsible for ensuring that their usage complies with applicable laws, regulations, and ethical standards.

It is recommended only for users who understand the implications of operating uncensored language models.

Deployment Notes

Best suited for self-hosted or research environments
Not recommended for unattended public-facing services
Works well with standard transformer inference frameworks
Supports further fine-tuning and alignment layering if desired

Acknowledgements

Thanks to the Qwen development team for releasing the base architecture and to the open-source community for providing tools, evaluations, and infrastructure that make experimentation with large language models accessible.