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
Legacy / unsupported — superseded by FinStruct
Status as of 2026-07-27: this repository is retained for provenance and download continuity. It is not an active Uraion Labs product, is not used by FinStruct, and is not supported for production or financial-document workflows.
Why it was superseded:
- The prior card said the model was benchmarked on BFCL-v4 and IFEval, but this repository contains no scores, raw predictions, evaluation script, base-model comparison, or environment manifest. That statement is withdrawn as unverified.
- The root GGUF files are documented in the original card as having malformed tensor shapes because they were converted directly from NF4-packed weights. They are not recommended for llama.cpp, Ollama, or LM Studio.
- The Transformers NF4 path has not been independently reproduced in a clean, versioned evaluation and has no measured advantage over its Qwen base model.
- 7,794 Hub downloads were recorded at audit time. Download count does not prove successful loading, adoption, model quality, or customer use.
No stronger capability claim should be inferred from the retained files. The full pre-audit card is
preserved in LEGACY_CARD.md for historical transparency.
Current Uraion Labs work is FinStruct: auditable, local-first extraction of SEC filings with versioned schemas, evidence, abstention, raw predictions, and reproducible benchmarks. See uraionlabs.com.
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