Instructions to use byteshape/Qwen3.5-35B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use byteshape/Qwen3.5-35B-A3B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="byteshape/Qwen3.5-35B-A3B-GGUF") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("byteshape/Qwen3.5-35B-A3B-GGUF", dtype="auto") - llama-cpp-python
How to use byteshape/Qwen3.5-35B-A3B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="byteshape/Qwen3.5-35B-A3B-GGUF", filename="Qwen3.5-35B-A3B-IQ2_S-2.17bpw.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
- llama.cpp
How to use byteshape/Qwen3.5-35B-A3B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf byteshape/Qwen3.5-35B-A3B-GGUF:Q4_K_S # Run inference directly in the terminal: llama-cli -hf byteshape/Qwen3.5-35B-A3B-GGUF:Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf byteshape/Qwen3.5-35B-A3B-GGUF:Q4_K_S # Run inference directly in the terminal: llama-cli -hf byteshape/Qwen3.5-35B-A3B-GGUF:Q4_K_S
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 byteshape/Qwen3.5-35B-A3B-GGUF:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf byteshape/Qwen3.5-35B-A3B-GGUF:Q4_K_S
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 byteshape/Qwen3.5-35B-A3B-GGUF:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf byteshape/Qwen3.5-35B-A3B-GGUF:Q4_K_S
Use Docker
docker model run hf.co/byteshape/Qwen3.5-35B-A3B-GGUF:Q4_K_S
- LM Studio
- Jan
- vLLM
How to use byteshape/Qwen3.5-35B-A3B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "byteshape/Qwen3.5-35B-A3B-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": "byteshape/Qwen3.5-35B-A3B-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/byteshape/Qwen3.5-35B-A3B-GGUF:Q4_K_S
- SGLang
How to use byteshape/Qwen3.5-35B-A3B-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "byteshape/Qwen3.5-35B-A3B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "byteshape/Qwen3.5-35B-A3B-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 images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "byteshape/Qwen3.5-35B-A3B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "byteshape/Qwen3.5-35B-A3B-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" } } ] } ] }' - Ollama
How to use byteshape/Qwen3.5-35B-A3B-GGUF with Ollama:
ollama run hf.co/byteshape/Qwen3.5-35B-A3B-GGUF:Q4_K_S
- Unsloth Studio new
How to use byteshape/Qwen3.5-35B-A3B-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 byteshape/Qwen3.5-35B-A3B-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 byteshape/Qwen3.5-35B-A3B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for byteshape/Qwen3.5-35B-A3B-GGUF to start chatting
- Pi new
How to use byteshape/Qwen3.5-35B-A3B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf byteshape/Qwen3.5-35B-A3B-GGUF:Q4_K_S
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": "byteshape/Qwen3.5-35B-A3B-GGUF:Q4_K_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use byteshape/Qwen3.5-35B-A3B-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 byteshape/Qwen3.5-35B-A3B-GGUF:Q4_K_S
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 byteshape/Qwen3.5-35B-A3B-GGUF:Q4_K_S
Run Hermes
hermes
- Docker Model Runner
How to use byteshape/Qwen3.5-35B-A3B-GGUF with Docker Model Runner:
docker model run hf.co/byteshape/Qwen3.5-35B-A3B-GGUF:Q4_K_S
- Lemonade
How to use byteshape/Qwen3.5-35B-A3B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull byteshape/Qwen3.5-35B-A3B-GGUF:Q4_K_S
Run and chat with the model
lemonade run user.Qwen3.5-35B-A3B-GGUF-Q4_K_S
List all available models
lemonade list
Please do 27B next
Hi! I was surprised to see you did Qwen3.5 35B A3B before the 27B dense.
The MOE model can for many people be ran at a high quant with decent speed using MOE offloading.
Using CPU layer offloading for the 27B dense model, however, slows it down to the point of making it practically useless in many cases.
I had been waiting excitingly for your next quant, since I thought it must be the 27B model.
Please do that one next!
Love your work. Your devstral 24B quant really gave us something beautiful!
Hey! Thank you for the kind words. We're slowly rolling out these models in the best way we can. We've been quite busy lately with many things, and our time is very limited since we're only 4 people working. Can't promise much, but we will try to release 27B!
Is gemma-4-26B-A4B coming? Your quantized models are truly performing excellently.
Is gemma-4-26B-A4B coming? Your quantized models are truly performing excellently.
@Kamil21322 We can't really tell you because we ourselves don't know exactly what's coming next. Right now we're working on some side stuff to quants and it's taking most of our time sadly, but what we can promise is that we'll try :(
Good job with the model, will make low memory people happy. Although for those who care about capability over speed, this is no match for qwen3.5-27b.
I would want to request the 122b or 397b, to make a quant optimized for gpu + cpu inference, at 4bit it would be possible for many to run it at home, on 128/256gb ram + 1-2 gpus. But let's wait a bit in case they decided to release qwen3.6. Also, working on a properly uncensored model could add extra value to it.