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
Download llama.cpp/tools/ui/src/lib/components/app/dialogs/DialogModelNotAvailable.svelte from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 2.6 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/tools/ui/src/lib/components/app/dialogs/DialogModelNotAvailable.svelte
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/tools/ui/src/lib/components/app/dialogs/DialogModelNotAvailable.svelte
-
curl -L -o DialogModelNotAvailable.svelte https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/tools/ui/src/lib/components/app/dialogs/DialogModelNotAvailable.svelte
2.6 kB
| <script lang="ts"> | |
| import * as AlertDialog from '$lib/components/ui/alert-dialog'; | |
| import { AlertTriangle, ArrowRight } from '@lucide/svelte'; | |
| import { goto } from '$app/navigation'; | |
| import { page } from '$app/state'; | |
| interface Props { | |
| open: boolean; | |
| modelName: string; | |
| availableModels?: string[]; | |
| onOpenChange?: (open: boolean) => void; | |
| } | |
| let { open = $bindable(), modelName, availableModels = [], onOpenChange }: Props = $props(); | |
| function handleOpenChange(newOpen: boolean) { | |
| open = newOpen; | |
| onOpenChange?.(newOpen); | |
| } | |
| function handleSelectModel(model: string) { | |
| // Build URL with selected model, preserving other params | |
| const url = new URL(page.url); | |
| url.searchParams.set('model', model); | |
| handleOpenChange(false); | |
| goto(url.toString()); | |
| } | |
| </script> | |
| <AlertDialog.Root {open} onOpenChange={handleOpenChange}> | |
| <AlertDialog.Content class="max-w-lg"> | |
| <AlertDialog.Header> | |
| <AlertDialog.Title class="flex items-center gap-2"> | |
| <AlertTriangle class="h-5 w-5 text-amber-500" /> | |
| Model Not Available | |
| </AlertDialog.Title> | |
| <AlertDialog.Description> | |
| The requested model could not be found. Select an available model to continue. | |
| </AlertDialog.Description> | |
| </AlertDialog.Header> | |
| <div class="space-y-3"> | |
| <div class="rounded-lg border border-amber-500/40 bg-amber-500/10 px-4 py-3 text-sm"> | |
| <p class="font-medium text-amber-600 dark:text-amber-400"> | |
| Requested: <code class="rounded bg-amber-500/20 px-1.5 py-0.5">{modelName}</code> | |
| </p> | |
| </div> | |
| {#if availableModels.length > 0} | |
| <div class="text-sm"> | |
| <p class="mb-2 font-medium text-muted-foreground">Select an available model:</p> | |
| <div class="max-h-48 space-y-1 overflow-y-auto rounded-md border p-1"> | |
| {#each availableModels as model (model)} | |
| <button | |
| type="button" | |
| class="group flex w-full items-center justify-between gap-2 rounded-sm px-3 py-2 text-left text-sm transition-colors hover:bg-accent hover:text-accent-foreground" | |
| onclick={() => handleSelectModel(model)} | |
| > | |
| <span class="min-w-0 truncate font-mono text-xs">{model}</span> | |
| <ArrowRight | |
| class="h-4 w-4 shrink-0 text-muted-foreground opacity-0 transition-opacity group-hover:opacity-100" | |
| /> | |
| </button> | |
| {/each} | |
| </div> | |
| </div> | |
| {/if} | |
| </div> | |
| <AlertDialog.Footer> | |
| <AlertDialog.Action onclick={() => handleOpenChange(false)}>Cancel</AlertDialog.Action> | |
| </AlertDialog.Footer> | |
| </AlertDialog.Content> | |
| </AlertDialog.Root> | |