Instructions to use 0xSero/Qwen3.6-28B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0xSero/Qwen3.6-28B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="0xSero/Qwen3.6-28B-GGUF",
	filename="model.bf16.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 0xSero/Qwen3.6-28B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf 0xSero/Qwen3.6-28B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf 0xSero/Qwen3.6-28B-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 0xSero/Qwen3.6-28B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf 0xSero/Qwen3.6-28B-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 0xSero/Qwen3.6-28B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf 0xSero/Qwen3.6-28B-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 0xSero/Qwen3.6-28B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 0xSero/Qwen3.6-28B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/0xSero/Qwen3.6-28B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use 0xSero/Qwen3.6-28B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0xSero/Qwen3.6-28B-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": "0xSero/Qwen3.6-28B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/0xSero/Qwen3.6-28B-GGUF:Q4_K_M

Ollama
How to use 0xSero/Qwen3.6-28B-GGUF with Ollama:
```
ollama run hf.co/0xSero/Qwen3.6-28B-GGUF:Q4_K_M
```

Unsloth Studio

How to use 0xSero/Qwen3.6-28B-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 0xSero/Qwen3.6-28B-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 0xSero/Qwen3.6-28B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for 0xSero/Qwen3.6-28B-GGUF to start chatting

How to use 0xSero/Qwen3.6-28B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf 0xSero/Qwen3.6-28B-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": "0xSero/Qwen3.6-28B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use 0xSero/Qwen3.6-28B-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 0xSero/Qwen3.6-28B-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 0xSero/Qwen3.6-28B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use 0xSero/Qwen3.6-28B-GGUF with Docker Model Runner:
```
docker model run hf.co/0xSero/Qwen3.6-28B-GGUF:Q4_K_M
```

Lemonade

How to use 0xSero/Qwen3.6-28B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 0xSero/Qwen3.6-28B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-28B-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.6-28B-GGUF

File size: 1,499 Bytes

cd812b6

---
license: mit
pipeline_tag: text-generation
base_model_relation: quantized
library_name: gguf
tags:
- gguf
- qwen3.6
- reap
---

> [!TIP]
> **[Support this work →](https://donate.sybilsolutions.ai)** · [X](https://x.com/0xsero) · [GitHub](https://github.com/0xsero) · [REAP paper](https://arxiv.org/abs/2510.13999) · [Cerebras REAP](https://huggingface.co/collections/cerebras/cerebras-reap)

# Qwen3.6-28B-GGUF

GGUF quantization of the base model.

## At a glance

| | |
|---|---|
| Base model | — |
| Format | GGUF |
| Total params | **28B** |
| Active / token | 3B |
| Experts / layer | — |
| Layers | — |
| Hidden size | — |
| Context | — |
| On-disk size | 147 GB |

## Which variant should I pick?

| Variant | Format | Link |
|---|---|---|
| `Qwen3.6-28B` | BF16 | [link](https://huggingface.co/0xSero/Qwen3.6-28B) |
| `Qwen3.6-28B-GGUF` **(this)** | GGUF | [link](https://huggingface.co/0xSero/Qwen3.6-28B-GGUF) |
| `Qwen3.6-35B-GGUF` | GGUF | [link](https://huggingface.co/0xSero/Qwen3.6-35B-GGUF) |

## License & citation
License inherited from the base model.

```bibtex
@misc{lasby2025reap,
  title  = {REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression},
  author = {Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa},
  year   = {2025}, eprint = {2510.13999}, archivePrefix = {arXiv}
}
```

## Sponsors
Made possible by **NVIDIA · TNG Technology · Lambda · Prime Intellect · Hot Aisle**.