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/examples/lookup/lookup-merge.cpp from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 1.49 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/examples/lookup/lookup-merge.cpp
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/examples/lookup/lookup-merge.cpp
-
curl -L -o lookup-merge.cpp https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/examples/lookup/lookup-merge.cpp
1.49 kB
| static void print_usage(char* argv0) { | |
| fprintf(stderr, "Merges multiple lookup cache files into a single one.\n"); | |
| fprintf(stderr, "Usage: %s [--help] lookup_part_1.bin lookup_part_2.bin ... lookup_merged.bin\n", argv0); | |
| } | |
| int main(int argc, char ** argv){ | |
| std::setlocale(LC_NUMERIC, "C"); | |
| if (argc < 3) { | |
| print_usage(argv[0]); | |
| exit(1); | |
| } | |
| std::vector<std::string> args; | |
| args.resize(argc-1); | |
| for (int i = 0; i < argc-1; ++i) { | |
| args[i] = argv[i+1]; | |
| if (args[i] == "-h" || args[i] == "--help") { | |
| print_usage(argv[0]); | |
| exit(0); | |
| } | |
| } | |
| fprintf(stderr, "lookup-merge: loading file %s\n", args[0].c_str()); | |
| common_ngram_cache ngram_cache_merged = common_ngram_cache_load(args[0]); | |
| for (size_t i = 1; i < args.size()-1; ++i) { | |
| fprintf(stderr, "lookup-merge: loading file %s\n", args[i].c_str()); | |
| common_ngram_cache ngram_cache = common_ngram_cache_load(args[i]); | |
| common_ngram_cache_merge(ngram_cache_merged, ngram_cache); | |
| } | |
| fprintf(stderr, "lookup-merge: saving file %s\n", args.back().c_str()); | |
| common_ngram_cache_save(ngram_cache_merged, args.back()); | |
| } | |