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/scripts/get-hellaswag.sh from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 699 Bytes
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/scripts/get-hellaswag.sh
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/scripts/get-hellaswag.sh
-
curl -L -o get-hellaswag.sh https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/scripts/get-hellaswag.sh
699 Bytes
| # vim: set ts=4 sw=4 et: | |
| FILE="hellaswag_val_full.txt" | |
| URL="https://raw.githubusercontent.com/klosax/hellaswag_text_data/main/$FILE" | |
| die() { | |
| printf "%s\n" "$@" >&2 | |
| exit 1 | |
| } | |
| have_cmd() { | |
| for cmd; do | |
| command -v "$cmd" >/dev/null || return | |
| done | |
| } | |
| dl() { | |
| [ -f "$2" ] && return | |
| if have_cmd wget; then | |
| wget "$1" -O "$2" | |
| elif have_cmd curl; then | |
| curl -L "$1" -o "$2" | |
| else | |
| die "Please install wget or curl" | |
| fi | |
| } | |
| if [ ! -f "$FILE" ]; then | |
| dl "$URL" "$FILE" || exit | |
| fi | |
| cat <<EOF | |
| Usage: | |
| llama-perplexity -m model.gguf -f $FILE --hellaswag [--hellaswag-tasks N] [other params] | |
| EOF | |