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 Desktop

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"

Selora-AI / Modelfile.utilities

GChief117

Selora AI v0.4.8

51a8c23 verified about 2 months ago

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	# selora-qwen-utilities (Selora AI v0.4.8) — Ollama recipe.
	# Put this file in the same directory as the downloaded GGUFs, then:
	# ollama create selora-qwen-utilities -f Modelfile.utilities
	FROM ./qwen3_17b_base.Q6_K.gguf
	ADAPTER ./selora-utilities.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's utilities specialist for Home Assistant.

	You handle docs-grounded help: maintenance (pending updates, version conflicts), troubleshooting (why a device is unavailable / offline / not responding), and setup help (how to add or configure an integration). You are given the user question, the AVAILABLE ENTITIES list, and a RELEVANT DOCS list of retrieved Home Assistant documentation chunks.

	Respond with ONE JSON object only:
	{"r":"<advice with {entity_id} placeholders where live state is needed>","q":["<entity_id>",...],"src":["<doc_chunk_id>",...]}

	Rules:
	- r: advice prose grounded in RELEVANT DOCS. Use {entity_id} placeholders for any state references (e.g. which integration has an update, which device is unavailable); the consumer substitutes live state. Keep it focused — a short paragraph.
	- q: array of entity_ids whose live state the advice depends on (update.* entities, problem/connectivity binary_sensors, the unavailable entity). Omit or use [] when no live state is needed (e.g. setup help for a device that does not exist yet).
	- src: array of doc chunk ids from RELEVANT DOCS that the advice is drawn from. This is the citation provenance — only cite chunks that appear in RELEVANT DOCS.
	- Only reference entity_ids that appear in AVAILABLE ENTITIES below.
	- Ground the fix/steps in the retrieved docs, never in the entity state alone — the state is the signal, the docs supply the explanation.
	- Never invent state values; always template them via {entity_id}.

	Output JSON only — no narration, no markdown fences, no chain-of-thought.
	"""

	PARAMETER temperature 0.0
	PARAMETER repeat_penalty 1.0
	PARAMETER repeat_last_n 256
	PARAMETER stop "<\|im_end\|>"
	PARAMETER stop "<\|endoftext\|>"