Text Generation
Safetensors
GGUF
English
reasoning
reinforcement-learning
grpo
small-language-model
samsung-ennovatex
conversational
Instructions to use OmnipotentFool/Aurvion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use OmnipotentFool/Aurvion 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 OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: llama cli -hf OmnipotentFool/Aurvion:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: llama cli -hf OmnipotentFool/Aurvion: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 OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf OmnipotentFool/Aurvion: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 OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf OmnipotentFool/Aurvion:Q4_K_M
Use Docker
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use OmnipotentFool/Aurvion with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OmnipotentFool/Aurvion" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OmnipotentFool/Aurvion", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Ollama
How to use OmnipotentFool/Aurvion with Ollama:
ollama run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use OmnipotentFool/Aurvion with Docker Model Runner:
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Lemonade
How to use OmnipotentFool/Aurvion with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull OmnipotentFool/Aurvion:Q4_K_M
Run and chat with the model
lemonade run user.Aurvion-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download llama.cpp/.github/workflows/winget.yml from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 1.6 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/.github/workflows/winget.yml
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/.github/workflows/winget.yml
-
curl -L -o winget.yml https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/.github/workflows/winget.yml
1.6 kB
| name: Update Winget Package | |
| on: | |
| workflow_dispatch: # allows manual triggering | |
| schedule: | |
| - cron: '28 5 * * *' # Update every day at 5:28 UTC | |
| jobs: | |
| update: | |
| name: Update Winget Package | |
| runs-on: ubuntu-latest | |
| if: github.repository_owner == 'ggml-org' | |
| steps: | |
| - name: Install cargo binstall | |
| uses: cargo-bins/cargo-binstall@268643a6b5ea099f5718ee5cd3ff7dc89a5eb49b | |
| - name: Install komac | |
| run: | | |
| cargo binstall komac@2.16.0 -y | |
| - name: Find latest release | |
| id: find_latest_release | |
| uses: actions/github-script@v8 | |
| with: | |
| script: | | |
| const { data: releases } = await github.rest.repos.listReleases({ | |
| owner: context.repo.owner, | |
| repo: context.repo.repo, | |
| }); | |
| const { tag_name: version, assets: assets } = releases.find(({assets}) => assets.find(asset => asset.name.includes('win-vulkan'))); | |
| const { browser_download_url: asset_url } = assets.find(asset => asset.name.includes('win-vulkan')); | |
| console.log("Latest release:", version); | |
| core.setOutput('VERSION', version); | |
| core.setOutput('ASSETURL', asset_url); | |
| - name: Update manifest | |
| run: | | |
| echo "Updating manifest..." | |
| komac update --version ${{ steps.find_latest_release.outputs.VERSION }} \ | |
| --urls "${{ steps.find_latest_release.outputs.ASSETURL }}" \ | |
| --token ${{ secrets.WINGET_GITHUB_TOKEN }} \ | |
| --submit \ | |
| ggml.llamacpp | |