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
| // Smart pointers for ggml types | |
| // ggml | |
| struct ggml_context_deleter { void operator()(ggml_context * ctx) { ggml_free(ctx); } }; | |
| struct gguf_context_deleter { void operator()(gguf_context * ctx) { gguf_free(ctx); } }; | |
| typedef std::unique_ptr<ggml_context, ggml_context_deleter> ggml_context_ptr; | |
| typedef std::unique_ptr<gguf_context, gguf_context_deleter> gguf_context_ptr; | |
| // ggml-alloc | |
| struct ggml_gallocr_deleter { void operator()(ggml_gallocr_t galloc) { ggml_gallocr_free(galloc); } }; | |
| typedef std::unique_ptr<ggml_gallocr, ggml_gallocr_deleter> ggml_gallocr_ptr; | |
| // ggml-backend | |
| struct ggml_backend_deleter { void operator()(ggml_backend_t backend) { ggml_backend_free(backend); } }; | |
| struct ggml_backend_buffer_deleter { void operator()(ggml_backend_buffer_t buffer) { ggml_backend_buffer_free(buffer); } }; | |
| struct ggml_backend_event_deleter { void operator()(ggml_backend_event_t event) { ggml_backend_event_free(event); } }; | |
| struct ggml_backend_sched_deleter { void operator()(ggml_backend_sched_t sched) { ggml_backend_sched_free(sched); } }; | |
| typedef std::unique_ptr<ggml_backend, ggml_backend_deleter> ggml_backend_ptr; | |
| typedef std::unique_ptr<ggml_backend_buffer, ggml_backend_buffer_deleter> ggml_backend_buffer_ptr; | |
| typedef std::unique_ptr<ggml_backend_event, ggml_backend_event_deleter> ggml_backend_event_ptr; | |
| typedef std::unique_ptr<ggml_backend_sched, ggml_backend_sched_deleter> ggml_backend_sched_ptr; | |