Text Generation
GGUF
English
qwen
qwen3
lora
home-assistant
home-automation
smart-home
iot
instruction-tuned
tool-use
ollama
conversational
Instructions to use selorahomes/Selora-AI-LLM-14B 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 selorahomes/Selora-AI-LLM-14B 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-LLM-14B:Q5_K_S # Run inference directly in the terminal: llama cli -hf selorahomes/Selora-AI-LLM-14B:Q5_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf selorahomes/Selora-AI-LLM-14B:Q5_K_S # Run inference directly in the terminal: llama cli -hf selorahomes/Selora-AI-LLM-14B:Q5_K_S
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf selorahomes/Selora-AI-LLM-14B:Q5_K_S # Run inference directly in the terminal: ./build/bin/llama cli -hf selorahomes/Selora-AI-LLM-14B:Q5_K_S
Use Docker
docker model run hf.co/selorahomes/Selora-AI-LLM-14B:Q5_K_S
- Llama new
- LM Studio
- Jan
- vLLM
How to use selorahomes/Selora-AI-LLM-14B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "selorahomes/Selora-AI-LLM-14B" # 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-LLM-14B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/selorahomes/Selora-AI-LLM-14B:Q5_K_S
- Ollama
How to use selorahomes/Selora-AI-LLM-14B with Ollama:
ollama run hf.co/selorahomes/Selora-AI-LLM-14B:Q5_K_S
- Unsloth Desktop
- Pi
How to use selorahomes/Selora-AI-LLM-14B 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-LLM-14B:Q5_K_S
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-LLM-14B:Q5_K_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use selorahomes/Selora-AI-LLM-14B with Docker Model Runner:
docker model run hf.co/selorahomes/Selora-AI-LLM-14B:Q5_K_S
- Lemonade
How to use selorahomes/Selora-AI-LLM-14B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull selorahomes/Selora-AI-LLM-14B:Q5_K_S
Run and chat with the model
lemonade run user.Selora-AI-LLM-14B-Q5_K_S
List all available models
lemonade list
- Hermes Agent
How to use selorahomes/Selora-AI-LLM-14B 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-LLM-14B:Q5_K_S
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-LLM-14B:Q5_K_S
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use selorahomes/Selora-AI-LLM-14B 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-LLM-14B:Q5_K_S
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-LLM-14B:Q5_K_S" \ --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: 2,233 Bytes
0e58ca5 c9e249c 0e58ca5 c9e249c 0e58ca5 1069b80 0e58ca5 | 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 49 50 | # Ollama Modelfile — Selora AI LLM command specialist.
#
# GENERATED by deploy/sync_prompts.py from
# data-pipeline/prompts/command_system_prompt.txt — do not edit by hand;
# edit the source prompt and re-run `just sync-prompts`.
#
# Build: ollama create selora-command -f Modelfile.command
FROM ./qwen3_14b_base.Q5_K_S.gguf
ADAPTER ./selora-command.f16.gguf
# Qwen3 ChatML. /no_think suppresses the reasoning block so output is
# bare JSON; this must match training/train.py's build_prompt().
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
/no_think {{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
<think>
</think>
"""
SYSTEM """You are Selora AI's command specialist for Home Assistant.
Given a user command and the AVAILABLE ENTITIES list, respond with ONE JSON object only:
{"c":[{"s":"<service>","e":"<entity_id>","d":{<optional params>}}],"r":"<short confirmation>"}
Rules:
- c: ordered array of service calls. Calls execute in array order.
- s: HA service in "domain.action" form (e.g. "light.turn_on", "lock.lock", "media_player.play_media", "scene.turn_on").
- e: canonical entity_id from AVAILABLE ENTITIES. Never use the human alias — always the entity_id.
- d: service parameters object. Omit the d key entirely when there are no params (do not include "d":{}).
- r: ≤ 1 sentence past-tense confirmation describing what got done (e.g. "Kitchen light on.").
- The service domain (before the dot) must match the entity_id's domain. light.turn_on goes with light.* entities, lock.lock goes with lock.* entities, etc.
- For multi-target requests, produce one c entry per (service, entity_id) pair.
- When several entities fit and the request does not single one out, or it would lock or unlock several locks it does not name, act on none: {"c":[],"r":"Which <device> — <name>, <name> or <name>?"}.
Output JSON only — no narration, no markdown fences, no chain-of-thought."""
# Generation params — from the published bundle's params file.
PARAMETER temperature 0.0
PARAMETER repeat_penalty 1.0
PARAMETER repeat_last_n 256
PARAMETER num_predict 384
PARAMETER num_ctx 4096
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
|