Instructions to use Devlin-AI/Devlin-Alpha-22B-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 Devlin-AI/Devlin-Alpha-22B-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 Devlin-AI/Devlin-Alpha-22B-A3B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Devlin-AI/Devlin-Alpha-22B-A3B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Devlin-AI/Devlin-Alpha-22B-A3B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Devlin-AI/Devlin-Alpha-22B-A3B-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 Devlin-AI/Devlin-Alpha-22B-A3B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Devlin-AI/Devlin-Alpha-22B-A3B-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 Devlin-AI/Devlin-Alpha-22B-A3B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Devlin-AI/Devlin-Alpha-22B-A3B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Devlin-AI/Devlin-Alpha-22B-A3B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Devlin-AI/Devlin-Alpha-22B-A3B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Devlin-AI/Devlin-Alpha-22B-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": "Devlin-AI/Devlin-Alpha-22B-A3B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Devlin-AI/Devlin-Alpha-22B-A3B-GGUF:Q4_K_M
- Ollama
How to use Devlin-AI/Devlin-Alpha-22B-A3B-GGUF with Ollama:
ollama run hf.co/Devlin-AI/Devlin-Alpha-22B-A3B-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use Devlin-AI/Devlin-Alpha-22B-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 Devlin-AI/Devlin-Alpha-22B-A3B-GGUF:Q4_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": "Devlin-AI/Devlin-Alpha-22B-A3B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Devlin-AI/Devlin-Alpha-22B-A3B-GGUF with Docker Model Runner:
docker model run hf.co/Devlin-AI/Devlin-Alpha-22B-A3B-GGUF:Q4_K_M
- Lemonade
How to use Devlin-AI/Devlin-Alpha-22B-A3B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Devlin-AI/Devlin-Alpha-22B-A3B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Devlin-Alpha-22B-A3B-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Devlin-AI/Devlin-Alpha-22B-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 Devlin-AI/Devlin-Alpha-22B-A3B-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 Devlin-AI/Devlin-Alpha-22B-A3B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Devlin-AI/Devlin-Alpha-22B-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 Devlin-AI/Devlin-Alpha-22B-A3B-GGUF:Q4_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 "Devlin-AI/Devlin-Alpha-22B-A3B-GGUF:Q4_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"
Devlin Alpha 22B A3B
Devlin Alpha 22B A3B is a development version of Devlin Mini, a lightweight model focused on agentic tasks based on Qwen3.6 35B A3B. This version of the model is intended only for testing purposes and is not recommended for production. Benchmarks pending.
Table of Contents 📝
- ▶ Usage
- 📃 License
- 🙏 Acknowledgements
▶ Usage
1. Download Models
Download models using huggingface-cli:
pip install "huggingface_hub[cli]"
huggingface-cli download Devlin-AI/Devlin-Alpha-22B-A3B-GGUF --local-dir ./Devlin-Alpha-22B-A3B-GGUF
You can also download directly from this page.
2. Inference
To use these GGUF files, you'll need a compatible inference engine like llama.cpp or clients built on top of it (e.g., Ollama, LM Studio, KoboldCpp, text-generation-webui with a llama.cpp backend).
⚠️ Important: Always pass
--jinjawhen loading withllama.cppso the Qwen3.6 chat template is applied correctly. Without it, the model may emit malformed turns.
Note: Text-only, and there's no MTP head, so draft/MTP speculative-decoding flags don't apply.
llama.cpp (server)
llama-server -hf Devlin-AI/Devlin-Alpha-22B-A3B-GGUF --port 8000 -c 262144 --jinja
Recommended sampling parameters (thinking mode): temperature=1.0, top_p=0.95, top_k=20, with --chat-template-kwargs "{\"enable_thinking\":true,\"preserve_thinking\":true}". For precise coding tasks: temperature=0.6, top_p=0.95, top_k=20. For non-thinking mode: temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5, with --chat-template-kwargs "{\"enable_thinking\":false}".
Parsing Reasoning Traces
Responses begin with a <think> … </think> block containing the chain-of-thought, followed by the final answer. To split them:
if "</think>" in text:
reasoning, answer = text.split("</think>", 1)
reasoning = reasoning.replace("<think>", "").strip()
answer = answer.strip()
else:
reasoning, answer = "", text.strip()
Ollama
ollama run hf.co/Devlin-AI/Devlin-Alpha-22B-A3B-GGUF
📃 License
This model is a derivative work of Qwen/Qwen3.6-35B-A3B, licensed under the Apache 2.0 License, and is therefore distributed under the terms of the same license.
🙏 Acknowledgements
- Qwen Team for the base model:
- The llama.cpp project and its contributors for the GGUF format and the incredible tooling that makes local LLM inference accessible.
- Downloads last month
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Model tree for Devlin-AI/Devlin-Alpha-22B-A3B-GGUF
Base model
Qwen/Qwen3.6-35B-A3B