Instructions to use 0xSero/Qwen3.6-28B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
- Pi
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
metadata
license: mit
pipeline_tag: text-generation
base_model_relation: quantized
library_name: gguf
tags:
- gguf
- qwen3.6
- reap
Support this work → · X · GitHub · REAP paper · 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 |
Qwen3.6-28B-GGUF (this) |
GGUF | link |
Qwen3.6-35B-GGUF |
GGUF | link |
License & citation
License inherited from the base model.
@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.