Instructions to use mudler/Qwen3.5-122B-A10B-APEX-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 mudler/Qwen3.5-122B-A10B-APEX-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 mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16 # Run inference directly in the terminal: llama cli -hf mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16 # Run inference directly in the terminal: llama cli -hf mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
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 mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
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 mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
Use Docker
docker model run hf.co/mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
- LM Studio
- Jan
- Ollama
How to use mudler/Qwen3.5-122B-A10B-APEX-GGUF with Ollama:
ollama run hf.co/mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
- Unsloth Desktop
- Pi
How to use mudler/Qwen3.5-122B-A10B-APEX-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
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": "mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use mudler/Qwen3.5-122B-A10B-APEX-GGUF with Docker Model Runner:
docker model run hf.co/mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
- Lemonade
How to use mudler/Qwen3.5-122B-A10B-APEX-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
Run and chat with the model
lemonade run user.Qwen3.5-122B-A10B-APEX-GGUF-F16
List all available models
lemonade list
- Hermes Agent
How to use mudler/Qwen3.5-122B-A10B-APEX-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 mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
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 mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use mudler/Qwen3.5-122B-A10B-APEX-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
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 "mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16" \ --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"
The best 3.5 -122b so far
I am enjoying I-Quality , it is best so far!
Context awareness is perfect even for full context.
Only missing is MTP support. Can you relase one with mtp?
If you've downloaded the model already you can patch in the draft layers manually using the model here
I've used my modded version for a week+ now and it works great. The fact this model is a lot less neurotic makes the MTP boost hit where it really matters, in actual work, rather than fill up the context quicker with neurotic ramblings in the reasoning blocks.
thats cool!