Instructions to use Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF",
	filename="llm2-q8_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF:Q8_0

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 Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF:Q8_0

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 Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF:Q8_0

Use Docker

docker model run hf.co/Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-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": "Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF:Q8_0

Ollama
How to use Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF with Ollama:
```
ollama run hf.co/Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF:Q8_0
```

Unsloth Studio new

How to use Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-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 Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-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 Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF to start chatting

Docker Model Runner
How to use Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF with Docker Model Runner:
```
docker model run hf.co/Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF:Q8_0
```

Lemonade

How to use Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Pinkstack/DistilGPT-OSS-qwen3-4B-Q8_O-GGUF:Q8_0

Run and chat with the model

lemonade run user.DistilGPT-OSS-qwen3-4B-Q8_O-GGUF-Q8_0

List all available models

lemonade list

This is a official q8_0 quantized version of https://huggingface.co/Pinkstack/DistilGPT-OSS-qwen3-4B

What it is

Distil-gpt-oss-qwen3-4B is a Qwen3 4B-2507 thinking fine tune, it supports up to 256K tokens of input and output (aka total context) and can think for up to 65536 tokens when set to high reasoning effort. unlike the original qwen3, this model was fine-tuned on GPT-OSS reasoning outputs (unlike Deepseek r1 outputs which qwen3 was probably fine-tuned on for advanced reasoning). By fine-tuning on GPT-OSS outputs, the model was able to learn how to think efficiently, follow instructions better, and the new ability to think with a certain effort based on how much you want it to think.

⚠️This model is NOT as censored as the original GPT-OSS, we focused on performance rather than censorship. The model is still safety trained, it would just allow for more "creative" prompts, unlike GPT-OSS. We are not responsible for what the model generates.

Keep in mind, this is a community project and we are NOT related to qwen by Alibaba nor GPT-OSS by OpenAi.

Format

This is the chat format of this model (you can also check the Jinja template file in "Files and versions"):

<|im_start|>system
You are a helpful assistant


Reasoning effort: low<|im_end|>
<|im_start|>user
Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,<|im_end|>
<|im_start|>assistant
<think>
Just continue: 13,21,34,...
</think>
The Fibonacci sequence continues by adding the two preceding numbers.  
After **8** the next term is **13** (1 + 8 = 9 – 1 = 8 + 5 = 13), then **21** (8 + 13 = 21), followed by **34** (13 + 21 = 34), and so on.

So the sequence starts:  

**1, 1, 2, 3, 5, 8, 13, 21, 34, …**<|im_end|>

As you can see, you set the reasoning effort via the system prompt. We recommend going 2 lines down and only then putting "Reasoning effort: [low,medium,high]. For your information that output was generated by our model.