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
| /** | |
| * HTTP request inspection utilities for diagnostic logging. | |
| * These helpers extract metadata from fetch-style request arguments | |
| * without exposing sensitive payload data. | |
| */ | |
| export interface RequestBodySummary { | |
| kind: string; | |
| size?: number; | |
| } | |
| export function getRequestUrl(input: RequestInfo | URL): string { | |
| if (typeof input === 'string') { | |
| return input; | |
| } | |
| if (input instanceof URL) { | |
| return input.href; | |
| } | |
| return input.url; | |
| } | |
| export function getRequestMethod( | |
| input: RequestInfo | URL, | |
| init?: RequestInit, | |
| baseInit?: RequestInit | |
| ): string { | |
| if (init?.method) { | |
| return init.method; | |
| } | |
| if (typeof Request !== 'undefined' && input instanceof Request) { | |
| return input.method; | |
| } | |
| return baseInit?.method ?? 'GET'; | |
| } | |
| export function getRequestBody( | |
| input: RequestInfo | URL, | |
| init?: RequestInit | |
| ): BodyInit | null | undefined { | |
| if (init?.body !== undefined) { | |
| return init.body; | |
| } | |
| if (typeof Request !== 'undefined' && input instanceof Request) { | |
| return input.body; | |
| } | |
| return undefined; | |
| } | |
| export function summarizeRequestBody(body: BodyInit | null | undefined): RequestBodySummary { | |
| if (body == null) { | |
| return { kind: 'empty' }; | |
| } | |
| if (typeof body === 'string') { | |
| return { kind: 'string', size: body.length }; | |
| } | |
| if (body instanceof Blob) { | |
| return { kind: 'blob', size: body.size }; | |
| } | |
| if (body instanceof URLSearchParams) { | |
| return { kind: 'urlsearchparams', size: body.toString().length }; | |
| } | |
| if (body instanceof FormData) { | |
| return { kind: 'formdata' }; | |
| } | |
| if (body instanceof ArrayBuffer) { | |
| return { kind: 'arraybuffer', size: body.byteLength }; | |
| } | |
| if (ArrayBuffer.isView(body)) { | |
| return { kind: body.constructor.name, size: body.byteLength }; | |
| } | |
| return { kind: typeof body }; | |
| } | |
| export function formatDiagnosticErrorMessage(error: unknown): string { | |
| const message = error instanceof Error ? error.message : String(error); | |
| return message.includes('Failed to fetch') ? `${message} (check CORS?)` : message; | |
| } | |
| export function extractJsonRpcMethods(body: BodyInit | null | undefined): string[] | undefined { | |
| if (typeof body !== 'string') { | |
| return undefined; | |
| } | |
| try { | |
| const parsed = JSON.parse(body); | |
| const messages = Array.isArray(parsed) ? parsed : [parsed]; | |
| const methods = messages | |
| .map((message: Record<string, unknown>) => | |
| typeof message?.method === 'string' ? (message.method as string) : undefined | |
| ) | |
| .filter((method: string | undefined): method is string => Boolean(method)); | |
| return methods.length > 0 ? methods : undefined; | |
| } catch { | |
| return undefined; | |
| } | |
| } | |