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 Studio
How to use OmnipotentFool/Aurvion with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for OmnipotentFool/Aurvion to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for OmnipotentFool/Aurvion to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for OmnipotentFool/Aurvion to start chatting
- 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: 2,513 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 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | import { config } from '$lib/stores/settings.svelte';
import { CORS_PROXY_HEADER_PREFIX, REDACTED_HEADERS } from '$lib/constants';
import { redactValue } from './redact';
/**
* Get authorization headers for API requests
* Includes Bearer token if API key is configured
*/
export function getAuthHeaders(): Record<string, string> {
const currentConfig = config();
const apiKey = currentConfig.apiKey?.toString().trim();
return apiKey ? { Authorization: `Bearer ${apiKey}` } : {};
}
/**
* Get standard JSON headers with optional authorization
*/
export function getJsonHeaders(): Record<string, string> {
return {
'Content-Type': 'application/json',
...getAuthHeaders()
};
}
/**
* Sanitize HTTP headers by redacting sensitive values.
* Known sensitive headers (from REDACTED_HEADERS) and any extra headers
* specified by the caller are fully redacted. Headers listed in
* `partialRedactHeaders` are partially redacted, showing only the
* specified number of trailing characters.
*
* @param headers - Headers to sanitize
* @param extraRedactedHeaders - Additional header names to fully redact
* @param partialRedactHeaders - Map of header name -> number of trailing chars to keep visible
* @returns Object with header names as keys and (possibly redacted) values
*/
export function sanitizeHeaders(
headers?: HeadersInit,
extraRedactedHeaders?: Iterable<string>,
partialRedactHeaders?: Map<string, number>
): Record<string, string> {
if (!headers) {
return {};
}
const normalized = new Headers(headers);
const sanitized: Record<string, string> = {};
const redactedHeaders = new Set(
Array.from(extraRedactedHeaders ?? [], (header) => header.toLowerCase())
);
for (const [key, value] of normalized.entries()) {
const normalizedKey = key.toLowerCase();
const unproxiedKey = normalizedKey.startsWith(CORS_PROXY_HEADER_PREFIX)
? normalizedKey.slice(CORS_PROXY_HEADER_PREFIX.length)
: normalizedKey;
const partialChars =
partialRedactHeaders?.get(normalizedKey) ?? partialRedactHeaders?.get(unproxiedKey);
if (partialChars !== undefined) {
sanitized[key] = redactValue(value, partialChars);
} else if (
REDACTED_HEADERS.has(normalizedKey) ||
REDACTED_HEADERS.has(unproxiedKey) ||
redactedHeaders.has(normalizedKey) ||
redactedHeaders.has(unproxiedKey)
) {
sanitized[key] = redactValue(value);
} else {
sanitized[key] = value;
}
}
return sanitized;
}
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