Text Generation
Transformers
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iot
smart-home
home-assistant
tool-calling
agent
cybersecurity
devops
ollama
inference-pipeline
autonomous-agent
raspberry-pi
qwen3-coder
Instructions to use MrMoz33/tokioai-coder-iot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MrMoz33/tokioai-coder-iot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MrMoz33/tokioai-coder-iot")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MrMoz33/tokioai-coder-iot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MrMoz33/tokioai-coder-iot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MrMoz33/tokioai-coder-iot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MrMoz33/tokioai-coder-iot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MrMoz33/tokioai-coder-iot
- SGLang
How to use MrMoz33/tokioai-coder-iot 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 "MrMoz33/tokioai-coder-iot" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MrMoz33/tokioai-coder-iot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "MrMoz33/tokioai-coder-iot" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MrMoz33/tokioai-coder-iot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MrMoz33/tokioai-coder-iot with Docker Model Runner:
docker model run hf.co/MrMoz33/tokioai-coder-iot
Download config/example.env from MrMoz33/tokioai-coder-iot: direct link, hf CLI and curl.
- Browser
- Download file 1.32 kB
-
https://huggingface.co/MrMoz33/tokioai-coder-iot/resolve/main/config/example.env
- Command line
-
hf download hf://MrMoz33/tokioai-coder-iot/config/example.env
-
curl -L -o example.env https://huggingface.co/MrMoz33/tokioai-coder-iot/resolve/main/config/example.env
1.32 kB
| # TokioAI-Coder IoT Configuration | |
| # Copy this to ~/.tokioai/.env and fill in your values | |
| # ============================================ | |
| # Home Assistant | |
| # ============================================ | |
| # Get a long-lived access token from: | |
| # HA -> Profile -> Long-Lived Access Tokens -> Create Token | |
| HA_TOKEN=your-long-lived-access-token-here | |
| HA_URL=http://localhost:8123 | |
| # ============================================ | |
| # Ollama / Model | |
| # ============================================ | |
| # Where Ollama is running (default: localhost) | |
| OLLAMA_HOST=http://localhost:11434 | |
| # Which model to use | |
| TOKIOAI_MODEL=qwen3-coder | |
| # ============================================ | |
| # Engine Server | |
| # ============================================ | |
| ENGINE_PORT=8080 | |
| MAX_INVESTIGATION_DEPTH=8 | |
| MAX_INVESTIGATION_TIME=90 | |
| # ============================================ | |
| # Robot Vision (optional) | |
| # ============================================ | |
| # URL of the robot camera proxy (if you have one) | |
| # ROBOT_PROXY_URL=http://localhost:5002 | |
| # Vision model for image analysis | |
| # VISION_MODEL=moondream | |
| # ============================================ | |
| # Remote Targets (optional) | |
| # ============================================ | |
| # SSH config for execute_raspi, execute_gcp, etc. | |
| # RASPI_HOST=192.168.1.x | |
| # RASPI_USER=pi | |
| # GCP_HOST=your-gcp-vm-ip | |
| # GCP_USER=your-user | |