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
- 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
Aurvion / llama.cpp /tools /ui /src /lib /components /app /content /MarkdownContent /markdown-utils.ts
Download llama.cpp/tools/ui/src/lib/components/app/content/MarkdownContent/markdown-utils.ts from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 2.74 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/tools/ui/src/lib/components/app/content/MarkdownContent/markdown-utils.ts
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/tools/ui/src/lib/components/app/content/MarkdownContent/markdown-utils.ts
-
curl -L -o markdown-utils.ts https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/tools/ui/src/lib/components/app/content/MarkdownContent/markdown-utils.ts
2.74 kB
| /** | |
| * 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 }; | |
| } | |