Instructions to use DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-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 DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-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 DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_K_M # Run inference directly in the terminal: llama cli -hf DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_K_M # Run inference directly in the terminal: llama cli -hf DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_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 DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_K_M # Run inference directly in the terminal: ./llama-cli -hf DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_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 DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_K_M
Use Docker
docker model run hf.co/DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_K_M
- LM Studio
- Jan
- vLLM
How to use DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-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": "DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_K_M
- Ollama
How to use DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF with Ollama:
ollama run hf.co/DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_K_M
- Unsloth Desktop
- Pi
How to use DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_K_M
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": "DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF with Docker Model Runner:
docker model run hf.co/DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_K_M
- Lemonade
How to use DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_K_M
Run and chat with the model
lemonade run user.Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF-Q3_K_M
List all available models
lemonade list
- Hermes Agent
How to use DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-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 DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_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 DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_K_M
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 "DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF:Q3_K_M" \ --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"
Applying same technique to ablated model?
Hey, this is a really nice and deceptively speedy model, at least running on the latest LM Studio. Do you have any intention of doing the same with an ablated version of the base model at all?
Hey ;
Unclear at the moment, if abliterated version will stand up to 40x "changes" , however there are 20x and 5x abliterated.
TAMER VERSIONS:
These are also versions using "Brainstorm", but the adapter is both smaller, and less active.
20X: (links to GGUF quants at the repo)
https://huggingface.co/DavidAU/Qwen3-42B-A3B-Stranger-Thoughts-Deep20X
https://huggingface.co/DavidAU/Qwen3-42B-A3B-Stranger-Thoughts-Deep20x-Abliterated-Uncensored
5X: (links to GGUF quants at the repo)
https://huggingface.co/DavidAU/Qwen3-33B-A3B-Stranger-Thoughts-IPONDER
https://huggingface.co/DavidAU/Qwen3-33B-A3B-Stranger-Thoughts-IPONDER-Abliterated-Uncensored
Additional versions based on 16B-A3B - a pruned version of the 30B, but 64 experts:
20X: (links to GGUF quants at the repo; these ones are very ahh... modified.)
https://huggingface.co/DavidAU/Qwen3-22B-A3B-The-Harley-Quinn
https://huggingface.co/DavidAU/Qwen3-22B-A3B-The-Harley-Quinn-PUDDIN-Abliterated-Uncensored
5X: (links to GGUF quants at the repo)
https://huggingface.co/DavidAU/Qwen3-18B-A3B-Stranger-Thoughts-IPONDER
https://huggingface.co/DavidAU/Qwen3-18B-A3B-Stranger-Thoughts-IPONDER-Abliterated-Uncensored
Thanks for the info, appreciate it. Hope the 40x takes hold!
I'm also using mrademacher's q8 quant of Stranger Thoughts Deep20x, and that's extremely good too, albeit a bit slower. If you do manage to get it working with the 40x, I'd be keen to have a look and see how it works with MLX. Good luck with further experimentation!