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
File size: 2,308 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 | import { SvelteMap } from 'svelte/reactivity';
import type { ModelOption } from '$lib/types/models';
export interface ModelItem {
option: ModelOption;
flatIndex: number;
}
export interface OrgGroup {
orgName: string | null;
items: ModelItem[];
}
export interface GroupedModelOptions {
loaded: ModelItem[];
favorites: ModelItem[];
available: OrgGroup[];
}
export function filterModelOptions(options: ModelOption[], searchTerm: string): ModelOption[] {
const term = searchTerm.trim().toLowerCase();
if (!term) return options;
return options.filter(
(option) =>
option.model.toLowerCase().includes(term) ||
option.name?.toLowerCase().includes(term) ||
option.aliases?.some((alias: string) => alias.toLowerCase().includes(term)) ||
option.tags?.some((tag: string) => tag.toLowerCase().includes(term))
);
}
export function groupModelOptions(
filteredOptions: ModelOption[],
favoriteIds: Set<string>,
isModelLoaded: (model: string) => boolean
): GroupedModelOptions {
// Loaded models
const loaded: ModelItem[] = [];
for (let i = 0; i < filteredOptions.length; i++) {
if (isModelLoaded(filteredOptions[i].model)) {
loaded.push({ option: filteredOptions[i], flatIndex: i });
}
}
// Favorites (excluding loaded)
const loadedModelIds = new Set(loaded.map((item) => item.option.model));
const favorites: ModelItem[] = [];
for (let i = 0; i < filteredOptions.length; i++) {
if (
favoriteIds.has(filteredOptions[i].model) &&
!loadedModelIds.has(filteredOptions[i].model)
) {
favorites.push({ option: filteredOptions[i], flatIndex: i });
}
}
// Available models grouped by org (excluding loaded and favorites)
const available: OrgGroup[] = [];
const orgGroups = new SvelteMap<string, ModelItem[]>();
for (let i = 0; i < filteredOptions.length; i++) {
const option = filteredOptions[i];
if (loadedModelIds.has(option.model) || favoriteIds.has(option.model)) continue;
const key = option.parsedId?.orgName ?? '';
if (!orgGroups.has(key)) orgGroups.set(key, []);
orgGroups.get(key)!.push({ option, flatIndex: i });
}
for (const [orgName, items] of orgGroups) {
available.push({ orgName: orgName || null, items });
}
return { loaded, favorites, available };
}
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