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/DialogConversationSelection.svelte from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 1.76 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/tools/ui/src/lib/components/app/dialogs/DialogConversationSelection.svelte
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/tools/ui/src/lib/components/app/dialogs/DialogConversationSelection.svelte
-
curl -L -o DialogConversationSelection.svelte https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/tools/ui/src/lib/components/app/dialogs/DialogConversationSelection.svelte
1.76 kB
| <script lang="ts"> | |
| import * as Dialog from '$lib/components/ui/dialog'; | |
| import { ConversationSelection } from '$lib/components/app'; | |
| interface Props { | |
| conversations: DatabaseConversation[]; | |
| messageCountMap?: Map<string, number>; | |
| mode: 'export' | 'import'; | |
| onCancel: () => void; | |
| onConfirm: (selectedConversations: DatabaseConversation[]) => void; | |
| open?: boolean; | |
| } | |
| let { | |
| conversations, | |
| messageCountMap = new Map(), | |
| mode, | |
| onCancel, | |
| onConfirm, | |
| open = $bindable(false) | |
| }: Props = $props(); | |
| let conversationSelectionRef: ConversationSelection | undefined = $state(); | |
| let previousOpen = $state(false); | |
| $effect(() => { | |
| if (open && !previousOpen && conversationSelectionRef) { | |
| conversationSelectionRef.reset(); | |
| } else if (!open && previousOpen) { | |
| onCancel(); | |
| } | |
| previousOpen = open; | |
| }); | |
| </script> | |
| <Dialog.Root bind:open> | |
| <Dialog.Portal> | |
| <Dialog.Overlay class="z-[1000000]" /> | |
| <Dialog.Content class="z-[1000001] max-w-2xl"> | |
| <Dialog.Header> | |
| <Dialog.Title> | |
| Select Conversations to {mode === 'export' ? 'Export' : 'Import'} | |
| </Dialog.Title> | |
| <Dialog.Description> | |
| {#if mode === 'export'} | |
| Choose which conversations you want to export. Selected conversations will be downloaded | |
| as a JSON file. | |
| {:else} | |
| Choose which conversations you want to import. Selected conversations will be merged | |
| with your existing conversations. | |
| {/if} | |
| </Dialog.Description> | |
| </Dialog.Header> | |
| <ConversationSelection | |
| bind:this={conversationSelectionRef} | |
| {conversations} | |
| {messageCountMap} | |
| {mode} | |
| {onCancel} | |
| {onConfirm} | |
| /> | |
| </Dialog.Content> | |
| </Dialog.Portal> | |
| </Dialog.Root> | |