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: 1,918 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 | <script lang="ts">
import { fly } from 'svelte/transition';
import { Input } from '$lib/components/ui/input';
import { Label } from '$lib/components/ui/label';
interface Props {
name: string;
value: string;
suggestions?: string[];
isLoadingSuggestions?: boolean;
isAutocompleteActive?: boolean;
autocompleteIndex?: number;
onInput: (value: string) => void;
onKeydown: (event: KeyboardEvent) => void;
onBlur: () => void;
onFocus: () => void;
onSelectSuggestion: (value: string) => void;
}
let {
name,
value = '',
suggestions = [],
isLoadingSuggestions = false,
isAutocompleteActive = false,
autocompleteIndex = 0,
onInput,
onKeydown,
onBlur,
onFocus,
onSelectSuggestion
}: Props = $props();
</script>
<div class="relative grid gap-1">
<Label for="tpl-arg-{name}" class="mb-1 text-muted-foreground">
<span>
{name}
<span class="text-destructive">*</span>
</span>
{#if isLoadingSuggestions}
<span class="text-xs text-muted-foreground/50">...</span>
{/if}
</Label>
<Input
id="tpl-arg-{name}"
type="text"
{value}
oninput={(e) => onInput(e.currentTarget.value)}
onkeydown={onKeydown}
onblur={onBlur}
onfocus={onFocus}
placeholder="Enter {name}"
autocomplete="off"
/>
{#if isAutocompleteActive && suggestions.length > 0}
<div
class="absolute top-full right-0 left-0 z-10 mt-1 max-h-32 overflow-y-auto rounded-lg border border-border/50 bg-background shadow-lg"
transition:fly={{ y: -5, duration: 100 }}
>
{#each suggestions as suggestion, i (suggestion)}
<button
type="button"
onmousedown={() => onSelectSuggestion(suggestion)}
class="w-full px-3 py-1.5 text-left text-sm hover:bg-accent {i === autocompleteIndex
? 'bg-accent'
: ''}"
>
{suggestion}
</button>
{/each}
</div>
{/if}
</div>
|