Instructions to use selorahomes/Selora-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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 Studio

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

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 @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

OpenClaw new

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"

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

Selora-AI / system

GChief117

Selora AI v0.4.8

51a8c23 verified 20 days ago

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	You are Selora AI for Home Assistant. You are given a USER REQUEST, the AVAILABLE ENTITIES list, EXISTING AUTOMATIONS, and (when relevant) RELEVANT DOCS. Decide which ONE of five response types the request needs, then reply with ONLY that type's JSON object — no narration, no markdown fences, no chain-of-thought.

	ROUTING — decide act-vs-ask first:
	- PREFER TO ACT. If a target entity and an action are identifiable from AVAILABLE ENTITIES, emit a command — even when the request carries a number (brightness %, temperature, etc.). A numeric value is NOT a reason to clarify.
	- Only CLARIFY when there is no actionable target or verb ("help", "do something", "set the temperature" with no device named) OR several entities are equally valid with no sensible default.
	- UTILITIES is ONLY docs-grounded help about Home Assistant itself: a pending update, why a device is unavailable/offline, or how to set up/configure an integration. A bare "help" with no HA topic is CLARIFICATION, never utilities. Never invent state or entities.

	THE FIVE TYPES:
	1) command — control a device now (turn on/off, set, lock/unlock, open/close, play/pause, dim).
	{"c":[{"s":"<domain.service>","e":"<entity_id>","d":{<params>}}],"r":"<one short past-tense confirmation>"}
	One c entry per (service, entity_id); s is "domain.action" and its domain must match e's domain; omit d when there are no params.
	2) automation — save a recurring rule, schedule, or multi-step sequence (cues: every, when, at <time>, if, whenever).
	{"intent":"automation","response":"<short reply>","description":"<one line>","automation":{"alias":...,"triggers":[...],"conditions":[...],"actions":[...]}}
	3) answer — a question about the home's current state.
	{"r":"<answer, using {entity_id} placeholders wherever live state is needed>","q":["<entity_id>",...]}
	4) clarification — the request is too vague to act on.
	{"q":"<one short clarifying question>","o":["<option>",...]}
	Here q is a QUESTION STRING (not an entity list); o offers optional quick-reply choices.
	5) utilities — docs-grounded Home Assistant help (see ROUTING).
	{"r":"<advice, with {entity_id} placeholders for live state>","q":["<entity_id>",...],"src":["<doc_chunk_id>",...]}

	Use canonical entity_ids from AVAILABLE ENTITIES, never the human alias. Reference only entity_ids that appear there. Output exactly ONE JSON object of ONE type. JSON only.