Instructions to use unsloth/Qwen3.5-35B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use unsloth/Qwen3.5-35B-A3B-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
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 unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
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 unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
Use Docker
docker model run hf.co/unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
- LM Studio
- Jan
- vLLM
How to use unsloth/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 "unsloth/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": "unsloth/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/unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
- Ollama
How to use unsloth/Qwen3.5-35B-A3B-GGUF with Ollama:
ollama run hf.co/unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
- Unsloth Desktop
- Pi
How to use unsloth/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 serve -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use unsloth/Qwen3.5-35B-A3B-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
- Lemonade
How to use unsloth/Qwen3.5-35B-A3B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
Run and chat with the model
lemonade run user.Qwen3.5-35B-A3B-GGUF-UD-Q4_K_XL
List all available models
lemonade list
- Hermes Agent
How to use unsloth/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 serve -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
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 unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use unsloth/Qwen3.5-35B-A3B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Coding parameters used for Goose and Zed
The model card recommends this for coding tasks:
Thinking mode for precise coding tasks (e.g. WebDev): temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
I found that for the UD Q4 version, the model gets stuck in thinking on simple prompts like "Hi" or after a few steps on more complex topics. I just increased the presence penalty to 0.1 and it seemed to work well enough. I had it fix an old unfinished Python implementation of Pacman with Zed. This is the first medium-size model (<100B params) I use that does not struggle at all with Zed's edit_file tool. It works REALLY well.
Full params for coding on llama.cpp (latest from main branch build: 8148, that fixes the template warnings):
-fitt 100 --fit-ctx 131072 --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0.0 --repeat-penalty 1.0 --presence-penalty 0.1 --batch-size 2048 --ubatch-size 512 --n-predict 20000
That gives me 30-40 tok/sec on a RTX-5060. I figure having a repeat penalty in code is not ideal and could have side-effects. Right now I just get the occasional stoppage in the middle of a task and I don't feel like calling-up Ralph.
Does anyone have any better parameter combination for coding?
Same issue here, but the happened on 35BA3B UD-Q4_K_XL
I had to switch to Qwen3 Coder Next to continue the rest of the work.
I found that UD-Q6_K_XL works properly with --presence-penalty 0.0 as recommended by Qwen. That's what I'll go with. I get about 30 tokens/s on a RTX-5060 with 16G vram and these settings:
-fitt 100 --fit-ctx 65536 --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0.0 --repeat-penalty 1.0 --presence-penalty 0.0 --ubatch-size 256 --n-predict 20000
That leaves 23 MOE layers overflowing to RAM, from what I see that's about an additional 16G used by llama.cpp.