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: 1,810 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 | <script lang="ts">
import { AlertTriangle, Server } from '@lucide/svelte';
import { Badge } from '$lib/components/ui/badge';
import { Button } from '$lib/components/ui/button';
import { serverProps, serverLoading, serverError } from '$lib/stores/server.svelte';
import { singleModelName } from '$lib/stores/models.svelte';
interface Props {
class?: string;
showActions?: boolean;
}
let { class: className = '', showActions = false }: Props = $props();
let error = $derived(serverError());
let loading = $derived(serverLoading());
let model = $derived(singleModelName());
let serverData = $derived(serverProps());
function getStatusColor() {
if (loading) return 'bg-yellow-500';
if (error) return 'bg-red-500';
if (serverData) return 'bg-green-500';
return 'bg-gray-500';
}
function getStatusText() {
if (loading) return 'Connecting...';
if (error) return 'Connection Error';
if (serverData) return 'Connected';
return 'Unknown';
}
</script>
<div class="flex items-center space-x-3 {className}">
<div class="flex items-center space-x-2">
<div class="h-2 w-2 rounded-full {getStatusColor()}"></div>
<span class="text-sm text-muted-foreground">{getStatusText()}</span>
</div>
{#if serverData && !error}
<Badge variant="outline" class="text-xs">
<Server class="mr-1 h-3 w-3" />
{model || 'Unknown Model'}
</Badge>
{#if serverData?.default_generation_settings?.n_ctx}
<Badge variant="secondary" class="text-xs">
ctx: {serverData.default_generation_settings.n_ctx.toLocaleString()}
</Badge>
{/if}
{/if}
{#if showActions && error}
<Button variant="outline" size="sm" class="text-destructive">
<AlertTriangle class="h-4 w-4" />
{error}
</Button>
{/if}
</div>
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