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: 2,149 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 | /**
* 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;
}
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