Instructions to use vorenthiclabs/vorenthos-architect-31b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use vorenthiclabs/vorenthos-architect-31b 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 vorenthiclabs/vorenthos-architect-31b
# Run inference directly in the terminal:
llama cli -hf vorenthiclabs/vorenthos-architect-31b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vorenthiclabs/vorenthos-architect-31b
# Run inference directly in the terminal:
llama cli -hf vorenthiclabs/vorenthos-architect-31b

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 vorenthiclabs/vorenthos-architect-31b
# Run inference directly in the terminal:
./llama-cli -hf vorenthiclabs/vorenthos-architect-31b

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 vorenthiclabs/vorenthos-architect-31b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vorenthiclabs/vorenthos-architect-31b

Use Docker

docker model run hf.co/vorenthiclabs/vorenthos-architect-31b

LM Studio
Jan

vLLM

How to use vorenthiclabs/vorenthos-architect-31b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vorenthiclabs/vorenthos-architect-31b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vorenthiclabs/vorenthos-architect-31b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vorenthiclabs/vorenthos-architect-31b

Ollama
How to use vorenthiclabs/vorenthos-architect-31b with Ollama:
```
ollama run hf.co/vorenthiclabs/vorenthos-architect-31b
```

Unsloth Studio

How to use vorenthiclabs/vorenthos-architect-31b 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 vorenthiclabs/vorenthos-architect-31b 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 vorenthiclabs/vorenthos-architect-31b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vorenthiclabs/vorenthos-architect-31b to start chatting

How to use vorenthiclabs/vorenthos-architect-31b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vorenthiclabs/vorenthos-architect-31b

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": "vorenthiclabs/vorenthos-architect-31b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use vorenthiclabs/vorenthos-architect-31b with Docker Model Runner:
```
docker model run hf.co/vorenthiclabs/vorenthos-architect-31b
```

Lemonade

How to use vorenthiclabs/vorenthos-architect-31b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vorenthiclabs/vorenthos-architect-31b

Run and chat with the model

lemonade run user.vorenthos-architect-31b-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use vorenthiclabs/vorenthos-architect-31b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vorenthiclabs/vorenthos-architect-31b

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 vorenthiclabs/vorenthos-architect-31b

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vorenthiclabs/vorenthos-architect-31b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vorenthiclabs/vorenthos-architect-31b

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 "vorenthiclabs/vorenthos-architect-31b" \
  --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"

vorenthos-architect-31b — Ollama Export

Exported from a local Ollama installation and uploaded to the Hugging Face Hub by vorenthiclabs.

Model Details

Field	Value
Base model	`claude`
Finetuned model	`vorenthos-architect-31b`
Tag / variant	`latest`
Model type	Text Generation
Format	GGUF (llama.cpp-compatible)
Total size	11.26 GB
Layers	3

Quick Start

With Ollama (recommended)

ollama pull vorenthos-architect-31b
ollama run vorenthos-architect-31b

With llama.cpp / llama-cpp-python (GGUF)

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="vorenthiclabs/vorenthos-architect-31b",
    filename="*.gguf",
)
output = llm("Hello, who are you?", max_tokens=256)
print(output["choices"][0]["text"])

With Hugging Face `transformers` + GGUF support

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("vorenthiclabs/vorenthos-architect-31b")
model     = AutoModelForCausalLM.from_pretrained("vorenthiclabs/vorenthos-architect-31b")

File Structure

File	Description
`config.json`	Ollama model configuration / metadata
`model-*.gguf`	Quantised weights in GGUF format
`tokenizer.jinja`	Chat template
`params.json`	Generation parameters (temperature, top-p, …)
`system_prompt.txt`	Default system prompt embedded in the model

License

Please check the original model's license before redistribution.
This upload is provided as-is for research and experimentation.

About vorenthiclabs

Visit us at https://huggingface.co/vorenthiclabs.

Downloads last month: 187

GGUF

Model size

31B params

Architecture

qwen3moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

vorenthos-architect-31b — Ollama Export

Model Details

Quick Start

With Ollama (recommended)

With llama.cpp / llama-cpp-python (GGUF)

With Hugging Face transformers + GGUF support

File Structure

License

About vorenthiclabs

With Hugging Face `transformers` + GGUF support