Instructions to use bartowski/Qwen_Qwen3.5-397B-A17B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use bartowski/Qwen_Qwen3.5-397B-A17B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bartowski/Qwen_Qwen3.5-397B-A17B-GGUF", filename="Qwen_Qwen3.5-397B-A17B-IQ1_M/Qwen_Qwen3.5-397B-A17B-IQ1_M-00001-of-00003.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use bartowski/Qwen_Qwen3.5-397B-A17B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartowski/Qwen_Qwen3.5-397B-A17B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/Qwen_Qwen3.5-397B-A17B-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 bartowski/Qwen_Qwen3.5-397B-A17B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/Qwen_Qwen3.5-397B-A17B-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 bartowski/Qwen_Qwen3.5-397B-A17B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/Qwen_Qwen3.5-397B-A17B-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 bartowski/Qwen_Qwen3.5-397B-A17B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/Qwen_Qwen3.5-397B-A17B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/Qwen_Qwen3.5-397B-A17B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/Qwen_Qwen3.5-397B-A17B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/Qwen_Qwen3.5-397B-A17B-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": "bartowski/Qwen_Qwen3.5-397B-A17B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/bartowski/Qwen_Qwen3.5-397B-A17B-GGUF:Q4_K_M
- Ollama
How to use bartowski/Qwen_Qwen3.5-397B-A17B-GGUF with Ollama:
ollama run hf.co/bartowski/Qwen_Qwen3.5-397B-A17B-GGUF:Q4_K_M
- Unsloth Studio
How to use bartowski/Qwen_Qwen3.5-397B-A17B-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 bartowski/Qwen_Qwen3.5-397B-A17B-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 bartowski/Qwen_Qwen3.5-397B-A17B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bartowski/Qwen_Qwen3.5-397B-A17B-GGUF to start chatting
- Pi
How to use bartowski/Qwen_Qwen3.5-397B-A17B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf bartowski/Qwen_Qwen3.5-397B-A17B-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": "bartowski/Qwen_Qwen3.5-397B-A17B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use bartowski/Qwen_Qwen3.5-397B-A17B-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 bartowski/Qwen_Qwen3.5-397B-A17B-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 bartowski/Qwen_Qwen3.5-397B-A17B-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use bartowski/Qwen_Qwen3.5-397B-A17B-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/Qwen_Qwen3.5-397B-A17B-GGUF:Q4_K_M
- Lemonade
How to use bartowski/Qwen_Qwen3.5-397B-A17B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/Qwen_Qwen3.5-397B-A17B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen_Qwen3.5-397B-A17B-GGUF-Q4_K_M
List all available models
lemonade list
Thanks for the quantizations, can we get MTP Qwen 3.5 397B GGUF?
Thanks for the quantizations, can we get MTP Qwen 3.5 397B GGUF?
Because Llama cpp merged MTP branch into the master branch yesterday
Yes I'm looking into it, I plan to release as a separate MTP file similar to how mmproj is done, just testing it myself to make sure it works as expected and so I have instructions on how to use it!
Thanks for updating this model with MTP! A bit sad that I can't fully offload the IQ1_M anymore, as when you get to these smaller quants every little bit helps accuracy!
I tested the IQ1_S, and it's creating good outputs, at great speeds. Model is split across 1x4090, 3x3090, full GPU offload, PP = 409.23 tokens/s, TG = 68.35 t/s.
For comparison I was testing the IQ1_M last night, without MTP and it was around 40 t/s TG. still fast, but this is a welcome boost!
I'm going to test out a higher quant with MTP enabled and see how that fares, maybe I can still retain usable TG while getting good accuracy! We'll see -
EDIT
IQ4_XS + -cpu-moe in combination with --spec-draft-cpu-moe results in very usable speeds, at a much more desirable quant! Lower PP was expected.
Final results for now:
IQ1_S w/ full GPU offload // PP = 409.23 tokens/s, TG = 68.35 t/s
IQ4_XS w/ -cpu-moe parameters // PP = 61.72 tokens/s, TG = 24.14 t/s
Final EDIT:
ik_llama.cpp is much better for partial CPU offload, resulting in even better IQ4_XS numbers.
ik_llama.cpp // IQ4_XS
PP = 154.67 t/s
TG = 33.70 t/s