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/tools/ui/src/lib/constants/cache.ts from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 1.37 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/tools/ui/src/lib/constants/cache.ts
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/tools/ui/src/lib/constants/cache.ts
-
curl -L -o cache.ts https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/tools/ui/src/lib/constants/cache.ts
1.37 kB
| /** | |
| * Cache configuration constants | |
| */ | |
| /** | |
| * Default TTL (Time-To-Live) for cache entries in milliseconds | |
| * @default 5 minutes | |
| */ | |
| export const DEFAULT_CACHE_TTL_MS = 5 * 60 * 1000; | |
| /** | |
| * Default maximum number of entries in a cache | |
| * @default 100 | |
| */ | |
| export const DEFAULT_CACHE_MAX_ENTRIES = 100; | |
| /** | |
| * TTL for model props cache in milliseconds | |
| * Props don't change frequently, so we can cache them longer | |
| * @default 10 minutes | |
| */ | |
| export const MODEL_PROPS_CACHE_TTL_MS = 10 * 60 * 1000; | |
| /** | |
| * Maximum number of model props to cache | |
| * @default 50 | |
| */ | |
| export const MODEL_PROPS_CACHE_MAX_ENTRIES = 50; | |
| /** | |
| * Maximum number of MCP resources to cache | |
| * @default 50 | |
| */ | |
| export const MCP_RESOURCE_CACHE_MAX_ENTRIES = 50; | |
| /** | |
| * TTL for MCP resource cache entries in milliseconds | |
| * @default 5 minutes | |
| */ | |
| export const MCP_RESOURCE_CACHE_TTL_MS = 5 * 60 * 1000; | |
| /** | |
| * Maximum number of inactive conversation states to keep in memory | |
| * States for conversations beyond this limit will be cleaned up | |
| * @default 10 | |
| */ | |
| export const MAX_INACTIVE_CONVERSATION_STATES = 10; | |
| /** | |
| * Maximum age (in ms) for inactive conversation states before cleanup | |
| * States older than this will be removed during cleanup | |
| * @default 30 minutes | |
| */ | |
| export const INACTIVE_CONVERSATION_STATE_MAX_AGE_MS = 30 * 60 * 1000; | |