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 Studio
How to use OmnipotentFool/Aurvion 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 OmnipotentFool/Aurvion 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 OmnipotentFool/Aurvion to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for OmnipotentFool/Aurvion to start chatting
- 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
| # Install pre-built version of llama.cpp | |
| | Install via | Windows | Mac | Linux | | |
| |-------------|---------|------|-------| | |
| | conda-forge | β | β | β | | |
| | Winget | β | | | | |
| | Homebrew | | β | β | | |
| | MacPorts | | β | | | |
| | Nix | | β | β | | |
| ## conda-forge (Windows, Mac and Linux) | |
| conda-forge provides builds for: | |
| - CUDA (Windows and Linux) | |
| - Vulkan (Windows and Linux) | |
| - Apple Metal (macOS) | |
| ```sh | |
| conda install -c conda-forge llama-cpp | |
| ``` | |
| ```sh | |
| mamba install -c conda-forge llama-cpp | |
| ``` | |
| ```sh | |
| # Project-local installation | |
| pixi add llama-cpp | |
| # Global installation | |
| pixi global install llama-cpp | |
| ``` | |
| This distribution is managed on [`conda-forge/llama-cpp-feedstock`](https://github.com/conda-forge/llama.cpp-feedstock/). | |
| Shall you have any problems, please open an issue on [its issue tracker](https://github.com/conda-forge/llama.cpp-feedstock/issues). | |
| ## Winget (Windows) | |
| ```sh | |
| winget install llama.cpp | |
| ``` | |
| The package is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggml-org/llama.cpp/issues/8188 | |
| ## Homebrew (Mac and Linux) | |
| ```sh | |
| brew install llama.cpp | |
| ``` | |
| The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggml-org/llama.cpp/discussions/7668 | |
| ## MacPorts (Mac) | |
| ```sh | |
| sudo port install llama.cpp | |
| ``` | |
| See also: https://ports.macports.org/port/llama.cpp/details/ | |
| ## Nix (Mac and Linux) | |
| ```sh | |
| nix profile install nixpkgs#llama-cpp | |
| ``` | |
| For flake enabled installs. | |
| Or | |
| ```sh | |
| nix-env --file '<nixpkgs>' --install --attr llama-cpp | |
| ``` | |
| For non-flake enabled installs. | |
| This expression is automatically updated within the [nixpkgs repo](https://github.com/NixOS/nixpkgs/blob/nixos-24.05/pkgs/by-name/ll/llama-cpp/package.nix#L164). | |