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
File size: 2,744 Bytes
6b9d0d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | /**
* Utility functions for markdown processing in MarkdownContent component.
*/
import type { RootContent as HastRootContent } from 'hast';
/**
* Generates a unique identifier for a HAST node based on its position.
* Used for stable block identification during incremental rendering.
* @param node - The HAST root content node
* @param indexFallback - Fallback index if position is unavailable
* @returns Unique string identifier for the node
*/
export function getHastNodeId(node: HastRootContent, indexFallback: number): string {
const position = node.position;
if (position?.start?.offset != null && position?.end?.offset != null) {
return `hast-${position.start.offset}-${position.end.offset}`;
}
return `${node.type}-${indexFallback}`;
}
/**
* Generates a hash for MDAST node based on its position.
* Used for cache lookup during incremental rendering.
*/
export function getMdastNodeHash(node: unknown, index: number): string {
const n = node as {
type?: string;
position?: { start?: { offset?: number }; end?: { offset?: number } };
};
if (n.position?.start?.offset != null && n.position?.end?.offset != null) {
return `${n.type}-${n.position.start.offset}-${n.position.end.offset}`;
}
return `${n.type}-idx${index}`;
}
/**
* Determines if the new content is an append (new content added to existing blocks).
* This is used to optimize cache reuse during streaming updates.
*
* @param newContent - The new markdown content
* @param previousContent - The previous markdown content to check against
* @returns true if the content appears to be an append operation
*/
export function isAppendMode(newContent: string, previousContent: string): boolean {
return previousContent.length > 0 && newContent.startsWith(previousContent);
}
export interface CodeInfo {
rawCode: string;
language: string;
}
/**
* Extracts code information from a button click target within a code block.
* @param target - The clicked button element
* @returns Object with rawCode and language, or null if extraction fails
*/
export function getCodeInfoFromTarget(target: HTMLElement): CodeInfo | null {
const wrapper = target.closest('.code-block-wrapper');
if (!wrapper) {
console.error('No wrapper found');
return null;
}
const codeElement = wrapper.querySelector<HTMLElement>('code[data-code-id]');
if (!codeElement) {
console.error('No code element found in wrapper');
return null;
}
const rawCode = codeElement.textContent ?? '';
const languageLabel = wrapper.querySelector<HTMLElement>('.code-language');
const language = languageLabel?.textContent?.trim() || 'text';
return { rawCode, language };
}
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