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
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -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
- Llama new
- 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/common/hf-cache.h from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 810 Bytes
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/common/hf-cache.h
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/common/hf-cache.h
-
curl -L -o hf-cache.h https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/common/hf-cache.h
810 Bytes
| // Ref: https://huggingface.co/docs/hub/local-cache.md | |
| namespace hf_cache { | |
| struct hf_file { | |
| std::string path; | |
| std::string url; | |
| std::string local_path; | |
| std::string final_path; | |
| std::string oid; | |
| std::string repo_id; | |
| }; | |
| using hf_files = std::vector<hf_file>; | |
| // Get files from HF API | |
| hf_files get_repo_files( | |
| const std::string & repo_id, | |
| const std::string & token | |
| ); | |
| hf_files get_cached_files(const std::string & repo_id = {}); | |
| // Create snapshot path (link or move/copy) and return it | |
| std::string finalize_file(const hf_file & file); | |
| // Remove the entire cached directory for a repo, returns true if removed | |
| bool remove_cached_repo(const std::string & repo_id); | |
| } // namespace hf_cache | |