Instructions to use sphaela/Qwen3.6-27B-AutoRound-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="sphaela/Qwen3.6-27B-AutoRound-GGUF", filename="Qwen3.6-27B-Q2_K_MIXED.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sphaela/Qwen3.6-27B-AutoRound-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 sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sphaela/Qwen3.6-27B-AutoRound-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 sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf sphaela/Qwen3.6-27B-AutoRound-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 sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
Use Docker
docker model run hf.co/sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with Ollama:
ollama run hf.co/sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
- Unsloth Studio
How to use sphaela/Qwen3.6-27B-AutoRound-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 sphaela/Qwen3.6-27B-AutoRound-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 sphaela/Qwen3.6-27B-AutoRound-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for sphaela/Qwen3.6-27B-AutoRound-GGUF to start chatting
- Pi
How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf sphaela/Qwen3.6-27B-AutoRound-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": "sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use sphaela/Qwen3.6-27B-AutoRound-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 sphaela/Qwen3.6-27B-AutoRound-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 sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with Docker Model Runner:
docker model run hf.co/sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
- Lemonade
How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3.6-27B-AutoRound-GGUF-Q4_K_M
List all available models
lemonade list
MTP support
Hi, is it possible to reacreate these quants with MTP support?
Me too, please.
Hello, I'd love to see MTP support as well!
I'll try to look into it, thanks for the suggestion :3
Yep this is the best small model that fits into 11GB GPU or reasonable with 32GB system ram. This with MTP should make it the best allrounder.
No MTP yet? :(
Hi guys thanks for the support :3 Autoround supports MTP with GGUF just merged so I'll try to requant the models with MTP enable.
YES Please! I'd be willing to donate even 5 bucks if you could make MTP model for Q5KM and Q4KM π€€
Do Q3 for poor too, with Autoroundbest Recipe Settings π
Hi everyone the requant has begun on my two DGX Spark and Qwen3.6-27B variant will be upload in 12hrs or so with the 35B following shortly :3
@soyalemujica I really appreciate but I currently don't have ways to accept donation ;-; thank you so much!
Hi guys sorry for the wait, the models is being upload right now! I literary stayed up all night to quantize everything. If anyone want to support me you could buy me a coffee over https://ko-fi.com/sphaela (I set one up per request :3)
Everything is uploaded, enjoy ;)
I'd like to thank you so very much! I have tipped you for making this possible, these autoround quants are the best I have ever tried, for some reason they think less, and come up with solutions quicker.
I'd like to thank you so very much! I have tipped you for making this possible, these autoround quants are the best I have ever tried, for some reason they think less, and come up with solutions quicker.
@soyalemujica Thank you so much for your generous tip! I'm glad you like it!
excellent work !