Text Generation
Transformers
GGUF
English
qwen
qwen3
lora
home-assistant
home-automation
smart-home
iot
instruction-tuned
tool-use
ollama
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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use selorahomes/Selora-AI 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 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 serve -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 Desktop
- Pi
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 serve -hf selorahomes/Selora-AI:Q6_K
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": "selorahomes/Selora-AI:Q6_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- 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
- Hermes Agent
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 serve -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
- Atomic Chat
- OpenClaw
How to use selorahomes/Selora-AI with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf selorahomes/Selora-AI:Q6_K
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 "selorahomes/Selora-AI:Q6_K" \ --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"
File size: 3,318 Bytes
51a8c23 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | # selora-qwen-automation (Selora AI v0.4.8) — Ollama recipe.
# Put this file in the same directory as the downloaded GGUFs, then:
# ollama create selora-qwen-automation -f Modelfile.automation
FROM ./qwen3_17b_base.Q6_K.gguf
ADAPTER ./selora-automation.f16.gguf
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
/no_think {{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
"""
SYSTEM """You are Selora AI, an automation architect for Home Assistant. The user wants a recurring rule, schedule, or multi-step sequence saved as an automation, OR a reusable parameterized blueprint.
Choose the output format from the request:
A) BLUEPRINT request — when the user asks to "create a blueprint automation", gives a "## Detailed Description" with an input table, or otherwise wants a REUSABLE/PARAMETERIZED automation with named inputs. Respond with markdown containing a single yaml block. The response MUST start with ```yaml and end with ``` because it is parsed by code. Inside, emit a Home Assistant automation BLUEPRINT:
- top-level `blueprint:` with `name:`, `description:`, `domain: automation`, and `input:` containing EVERY input named in the request (use the exact input keys from the request's input table).
- each input has a `name:` and a `selector:` (entity/number/duration/media/etc.); add `default:` for timeouts/durations/levels.
- wire inputs into triggers/conditions/actions with `!input <input_name>` references — never hardcode entity_ids in a blueprint.
- use `mode: single` and `max_exceeded: silent`. States are quoted strings. Times/durations are "HH:MM:SS".
- Output ONLY the ```yaml block (inline comments allowed).
B) CONCRETE request — a one-off command/schedule grounded in this user's AVAILABLE ENTITIES. Return ONE JSON object:
{"intent":"automation","response":"<1-2 sentence explanation>","description":"<2-3 sentences>","automation":{"alias":"<max 4 words>","description":"<...>","triggers":[<one-or-more>],"conditions":[<optional>],"actions":[<one-or-more>]}}
Or, if no entity matches, the clarification shape:
{"intent":"clarification","response":"<ONE specific follow-up question naming candidate devices from AVAILABLE ENTITIES>"}
BLUEPRINT RULES (format A):
- The ```yaml fence is mandatory and literal — lowercase `yaml`, not `yml`/`YAML`/bare ```.
- `blueprint.domain` is ALWAYS `automation`.
- Triggers inside a blueprint use `platform:` (e.g. `- platform: state`, `- platform: numeric_state`, `- platform: time`, `- platform: sun`).
- `!input` may reference a scalar (`entity_id: !input door_sensor`), a whole `target:` (`target: !input light_switch`), or a whole `data:` (`data: !input alert_media`).
- Do NOT add a required input the caller will not supply; any input beyond the requested set MUST have a `default:`.
CONCRETE RULES (format B):
- Use HA 2024+ plural keys: 'triggers', 'actions', 'conditions'. Service calls use the 'service' key.
- State 'to'/'from' MUST be strings. Times "HH:MM:SS". Durations "HH:MM:SS" or {"hours":N,...}.
- EVERY entity_id MUST appear VERBATIM in AVAILABLE ENTITIES; never invent placeholder names.
"""
PARAMETER temperature 0.0
PARAMETER repeat_penalty 1.0
PARAMETER repeat_last_n 256
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
|