Text Generation
GGUF
Safetensors
English
llama-cpp
lm-studio
ollama
local-llm
uraion-labs
uraion
agent
tool-calling
function-calling
qwen3.5
qlora
sft
trl
small-model
local-deployment
systems-research
hermes-function-calling
conversational
Instructions to use UraionLabs/Uraion-Agent-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use UraionLabs/Uraion-Agent-Small with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="UraionLabs/Uraion-Agent-Small", filename="Uraion-Agent-Small-F16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use UraionLabs/Uraion-Agent-Small 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 UraionLabs/Uraion-Agent-Small:Q4_K_M # Run inference directly in the terminal: llama cli -hf UraionLabs/Uraion-Agent-Small:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf UraionLabs/Uraion-Agent-Small:Q4_K_M # Run inference directly in the terminal: llama cli -hf UraionLabs/Uraion-Agent-Small:Q4_K_M
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 UraionLabs/Uraion-Agent-Small:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf UraionLabs/Uraion-Agent-Small:Q4_K_M
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 UraionLabs/Uraion-Agent-Small:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf UraionLabs/Uraion-Agent-Small:Q4_K_M
Use Docker
docker model run hf.co/UraionLabs/Uraion-Agent-Small:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use UraionLabs/Uraion-Agent-Small with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "UraionLabs/Uraion-Agent-Small" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UraionLabs/Uraion-Agent-Small", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/UraionLabs/Uraion-Agent-Small:Q4_K_M
- Ollama
How to use UraionLabs/Uraion-Agent-Small with Ollama:
ollama run hf.co/UraionLabs/Uraion-Agent-Small:Q4_K_M
- Unsloth Studio
How to use UraionLabs/Uraion-Agent-Small 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 UraionLabs/Uraion-Agent-Small 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 UraionLabs/Uraion-Agent-Small to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for UraionLabs/Uraion-Agent-Small to start chatting
- Pi
How to use UraionLabs/Uraion-Agent-Small with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf UraionLabs/Uraion-Agent-Small:Q4_K_M
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": "UraionLabs/Uraion-Agent-Small:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use UraionLabs/Uraion-Agent-Small with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf UraionLabs/Uraion-Agent-Small:Q4_K_M
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 UraionLabs/Uraion-Agent-Small:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use UraionLabs/Uraion-Agent-Small with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf UraionLabs/Uraion-Agent-Small:Q4_K_M
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 "UraionLabs/Uraion-Agent-Small:Q4_K_M" \ --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 UraionLabs/Uraion-Agent-Small with Docker Model Runner:
docker model run hf.co/UraionLabs/Uraion-Agent-Small:Q4_K_M
- Lemonade
How to use UraionLabs/Uraion-Agent-Small with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull UraionLabs/Uraion-Agent-Small:Q4_K_M
Run and chat with the model
lemonade run user.Uraion-Agent-Small-Q4_K_M
List all available models
lemonade list
Ctrl+K
Fix: Rebuilt all GGUF quants with correct BNB NF4 dequantization. Root cause was incorrect nested absmax formula (product vs sum) and nibble ordering. All quants now work with latest llama.cpp.
75ea3e9 verified