Text Generation
Transformers
GGUF
English
qwen
qwen3
lora
home-assistant
home-automation
smart-home
tool-use
conversational
Instructions to use selorahomes/Selora-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selorahomes/Selora-AI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="selorahomes/Selora-AI") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("selorahomes/Selora-AI", dtype="auto") - llama-cpp-python
How to use selorahomes/Selora-AI with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="selorahomes/Selora-AI", filename="qwen3_17b_base.Q6_K.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use selorahomes/Selora-AI with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf selorahomes/Selora-AI:Q6_K # Run inference directly in the terminal: llama-cli -hf selorahomes/Selora-AI:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf selorahomes/Selora-AI:Q6_K # Run inference directly in the terminal: llama-cli -hf selorahomes/Selora-AI:Q6_K
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 selorahomes/Selora-AI:Q6_K # Run inference directly in the terminal: ./llama-cli -hf selorahomes/Selora-AI:Q6_K
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 selorahomes/Selora-AI:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf selorahomes/Selora-AI:Q6_K
Use Docker
docker model run hf.co/selorahomes/Selora-AI:Q6_K
- LM Studio
- Jan
- vLLM
How to use selorahomes/Selora-AI with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "selorahomes/Selora-AI" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "selorahomes/Selora-AI", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/selorahomes/Selora-AI:Q6_K
- SGLang
How to use selorahomes/Selora-AI 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 "selorahomes/Selora-AI" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "selorahomes/Selora-AI", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "selorahomes/Selora-AI" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "selorahomes/Selora-AI", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use selorahomes/Selora-AI with Ollama:
ollama run hf.co/selorahomes/Selora-AI:Q6_K
- Unsloth Studio new
How to use selorahomes/Selora-AI with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for selorahomes/Selora-AI to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for selorahomes/Selora-AI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for selorahomes/Selora-AI to start chatting
- Pi new
How to use selorahomes/Selora-AI with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf selorahomes/Selora-AI:Q6_K
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "selorahomes/Selora-AI:Q6_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use selorahomes/Selora-AI with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf selorahomes/Selora-AI:Q6_K
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 selorahomes/Selora-AI:Q6_K
Run Hermes
hermes
- Docker Model Runner
How to use selorahomes/Selora-AI with Docker Model Runner:
docker model run hf.co/selorahomes/Selora-AI:Q6_K
- Lemonade
How to use selorahomes/Selora-AI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull selorahomes/Selora-AI:Q6_K
Run and chat with the model
lemonade run user.Selora-AI-Q6_K
List all available models
lemonade list
| # Ollama Modelfile for SeloraAI-Local / answer specialist (Qwen3 1.7B) | |
| # Build: ollama create selora-qwen-answer -f Modelfile.answers | |
| # Run: ollama run selora-qwen-answer | |
| FROM ../qwen3_17b_base.IQ4_XS.gguf | |
| ADAPTER ../qwen3_17b_answer.lora.gguf | |
| # Qwen3 chat template (ChatML, /no_think to suppress reasoning blocks for | |
| # short structured JSON output) | |
| TEMPLATE """{{ if .System }}<|im_start|>system | |
| {{ .System }}<|im_end|> | |
| {{ end }}{{ if .Prompt }}<|im_start|>user | |
| /no_think {{ .Prompt }}<|im_end|> | |
| {{ end }}<|im_start|>assistant | |
| """ | |
| # Trained per-specialist system prompt (matches current training data, | |
| # includes the query_state tool envelope). | |
| SYSTEM """You are Selora AI, a home automation assistant on Home Assistant. You CAN: control lights/climate/locks/switches, run scripts and scenes, set timers and reminders via timer/input_datetime entities, query device states, and create automations on request. Never say you are a "text-based AI" or that you cannot do something Home Assistant supports β describe how you would do it instead. | |
| Return ONE JSON object using one of these envelope shapes: | |
| ANSWER β for conversational questions, recommendations, or when AVAILABLE ENTITIES already has the full answer: | |
| {"intent":"answer","response":"<1-3 sentences>"} | |
| QUERY_STATE β for live state queries that need filtering by state/attribute: | |
| {"intent":"query_state","calls":[{"tool":"query_state","args":{"domain":"<domain>","filter":{"state":"<value>"}}}]} | |
| TOOL SCHEMA: | |
| - tool: "query_state" | |
| - args: | |
| domain (str, required): HA domain β light/switch/lock/cover/fan/media_player/climate/binary_sensor/sensor/person/device_tracker | |
| filter (dict, optional): | |
| state (str): match exact state ("on", "off", "locked", "open", "home", ...) | |
| entity_id (str): match a specific entity_id | |
| device_class (str): match HA device_class ("door", "window", "motion", ...) | |
| attribute (dict): match attribute key/value (e.g. {"hvac_mode": "heat"}) | |
| WHEN TO USE EACH: | |
| - query_state for "what's on?", "is X locked?", "how many windows are open?", "which thermostats are heating?". | |
| - answer for "what can you do?", "explain X", or when the catalog already gives a complete 1-3 sentence answer ("am I home?" β check person entity). | |
| RULES: | |
| - 1-3 sentences for answer. Add detail only if the user asked for it. | |
| - Ground answer responses in AVAILABLE ENTITIES β name actual friendly_names and current state values. | |
| - When naming a specific device in an answer, wrap its friendly_name in entity markers like [[entity:light.kitchen|Kitchen Lights]] so the panel renders it as a live tile. | |
| - Output ONLY the JSON object.""" | |
| # Generation params β matches what the integration sends + repeat_penalty for Qwen | |
| PARAMETER temperature 0.0 | |
| PARAMETER repeat_penalty 1.15 | |
| PARAMETER repeat_last_n 256 | |
| PARAMETER stop "<|im_end|>" | |
| PARAMETER stop "<|endoftext|>" | |