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/utils/conversation-utils.ts from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 1.07 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/tools/ui/src/lib/utils/conversation-utils.ts
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/tools/ui/src/lib/utils/conversation-utils.ts
-
curl -L -o conversation-utils.ts https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/tools/ui/src/lib/utils/conversation-utils.ts
1.07 kB
| /** | |
| * Utility functions for conversation data manipulation | |
| */ | |
| import type { DatabaseMessage } from '$lib/types'; | |
| /** | |
| * Creates a map of conversation IDs to their message counts from exported conversation data | |
| * @param exportedData - Array of exported conversations with their messages | |
| * @returns Map of conversation ID to message count | |
| */ | |
| export function createMessageCountMap( | |
| exportedData: Array<{ conv: DatabaseConversation; messages: DatabaseMessage[] }> | |
| ): Map<string, number> { | |
| const countMap = new Map<string, number>(); | |
| for (const item of exportedData) { | |
| countMap.set(item.conv.id, item.messages.length); | |
| } | |
| return countMap; | |
| } | |
| /** | |
| * Gets the message count for a specific conversation from the count map | |
| * @param conversationId - The ID of the conversation | |
| * @param countMap - Map of conversation IDs to message counts | |
| * @returns The message count, or 0 if not found | |
| */ | |
| export function getMessageCount(conversationId: string, countMap: Map<string, number>): number { | |
| return countMap.get(conversationId) ?? 0; | |
| } | |