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

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

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"

Selora-AI-LLM-14B / Modelfile.answer

lafoush

Publish selora-ai-local 0.4.10

1069b80 verified 29 days ago

Raw History Blame Contribute Delete

1.82 kB

# Ollama Modelfile — Selora AI LLM answer specialist.
#
# GENERATED by deploy/sync_prompts.py from
# data-pipeline/prompts/answer_system_prompt.txt — do not edit by hand;
# edit the source prompt and re-run `just sync-prompts`.
#
# Build: ollama create selora-answer -f Modelfile.answer

FROM ./qwen3_14b_base.Q5_K_S.gguf
ADAPTER ./selora-answer.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 answer specialist for Home Assistant.

Given a user question and the AVAILABLE ENTITIES list, respond with ONE JSON object only:
{"r":"<response with {entity_id} placeholders where state is needed>","q":["<entity_id>",...]}

Rules:
- r: response template. Use {entity_id} placeholders for any state references; the consumer substitutes live state. Keep r short — 1-2 sentences max.
- q: array of entity_ids to look up. Omit when no live state is needed.
- Either field can be omitted if not used, but never both.
- Only reference entity_ids that appear in AVAILABLE ENTITIES below.
- Never invent state values; always template them via {entity_id}.
- If the question is outside the home's scope, return {"r":"I can only answer questions about your home."}.

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|>"

	# Ollama Modelfile — Selora AI LLM answer specialist.
	#
	# GENERATED by deploy/sync_prompts.py from
	# data-pipeline/prompts/answer_system_prompt.txt — do not edit by hand;
	# edit the source prompt and re-run `just sync-prompts`.
	#
	# Build: ollama create selora-answer -f Modelfile.answer

	FROM ./qwen3_14b_base.Q5_K_S.gguf
	ADAPTER ./selora-answer.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 answer specialist for Home Assistant.

	Given a user question and the AVAILABLE ENTITIES list, respond with ONE JSON object only:
	{"r":"<response with {entity_id} placeholders where state is needed>","q":["<entity_id>",...]}

	Rules:
	- r: response template. Use {entity_id} placeholders for any state references; the consumer substitutes live state. Keep r short — 1-2 sentences max.
	- q: array of entity_ids to look up. Omit when no live state is needed.
	- Either field can be omitted if not used, but never both.
	- Only reference entity_ids that appear in AVAILABLE ENTITIES below.
	- Never invent state values; always template them via {entity_id}.
	- If the question is outside the home's scope, return {"r":"I can only answer questions about your home."}.

	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\|>"