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/model-names.ts from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 2.15 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/tools/ui/src/lib/utils/model-names.ts
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/tools/ui/src/lib/utils/model-names.ts
-
curl -L -o model-names.ts https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/tools/ui/src/lib/utils/model-names.ts
2.15 kB
| /** | |
| * Normalizes a model name by extracting the filename from a path, but preserves Hugging Face repository format. | |
| * | |
| * Handles both forward slashes (/) and backslashes (\) as path separators. | |
| * - If the model name has exactly one slash (org/model format), preserves the full "org/model" name | |
| * - If the model name has no slash or multiple slashes, extracts just the filename | |
| * - If the model name is just a filename (no path), returns it as-is. | |
| * | |
| * @param modelName - The model name or path to normalize | |
| * @returns The normalized model name | |
| * | |
| * @example | |
| * normalizeModelName('models/llama-3.1-8b') // Returns: 'llama-3.1-8b' (multiple slashes -> filename) | |
| * normalizeModelName('C:\\Models\\gpt-4') // Returns: 'gpt-4' (multiple slashes -> filename) | |
| * normalizeModelName('meta-llama/Llama-3.1-8B') // Returns: 'meta-llama/Llama-3.1-8B' (Hugging Face format) | |
| * normalizeModelName('simple-model') // Returns: 'simple-model' (no slash) | |
| * normalizeModelName(' spaced ') // Returns: 'spaced' | |
| * normalizeModelName('') // Returns: '' | |
| */ | |
| export function normalizeModelName(modelName: string): string { | |
| const trimmed = modelName.trim(); | |
| if (!trimmed) { | |
| return ''; | |
| } | |
| const segments = trimmed.split(/[\\/]/); | |
| // If we have exactly 2 segments (one slash), treat it as Hugging Face repo format | |
| // and preserve the full "org/model" format | |
| if (segments.length === 2) { | |
| const [org, model] = segments; | |
| const trimmedOrg = org?.trim(); | |
| const trimmedModel = model?.trim(); | |
| if (trimmedOrg && trimmedModel) { | |
| return `${trimmedOrg}/${trimmedModel}`; | |
| } | |
| } | |
| // For other cases (no slash, or multiple slashes), extract just the filename | |
| const candidate = segments.pop(); | |
| const normalized = candidate?.trim(); | |
| return normalized && normalized.length > 0 ? normalized : trimmed; | |
| } | |
| /** | |
| * Validates if a model name is valid (non-empty after normalization). | |
| * | |
| * @param modelName - The model name to validate | |
| * @returns true if valid, false otherwise | |
| */ | |
| export function isValidModelName(modelName: string): boolean { | |
| return normalizeModelName(modelName).length > 0; | |
| } | |