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
File size: 2,680 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 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | /**
* HTTP request inspection utilities for diagnostic logging.
* These helpers extract metadata from fetch-style request arguments
* without exposing sensitive payload data.
*/
export interface RequestBodySummary {
kind: string;
size?: number;
}
export function getRequestUrl(input: RequestInfo | URL): string {
if (typeof input === 'string') {
return input;
}
if (input instanceof URL) {
return input.href;
}
return input.url;
}
export function getRequestMethod(
input: RequestInfo | URL,
init?: RequestInit,
baseInit?: RequestInit
): string {
if (init?.method) {
return init.method;
}
if (typeof Request !== 'undefined' && input instanceof Request) {
return input.method;
}
return baseInit?.method ?? 'GET';
}
export function getRequestBody(
input: RequestInfo | URL,
init?: RequestInit
): BodyInit | null | undefined {
if (init?.body !== undefined) {
return init.body;
}
if (typeof Request !== 'undefined' && input instanceof Request) {
return input.body;
}
return undefined;
}
export function summarizeRequestBody(body: BodyInit | null | undefined): RequestBodySummary {
if (body == null) {
return { kind: 'empty' };
}
if (typeof body === 'string') {
return { kind: 'string', size: body.length };
}
if (body instanceof Blob) {
return { kind: 'blob', size: body.size };
}
if (body instanceof URLSearchParams) {
return { kind: 'urlsearchparams', size: body.toString().length };
}
if (body instanceof FormData) {
return { kind: 'formdata' };
}
if (body instanceof ArrayBuffer) {
return { kind: 'arraybuffer', size: body.byteLength };
}
if (ArrayBuffer.isView(body)) {
return { kind: body.constructor.name, size: body.byteLength };
}
return { kind: typeof body };
}
export function formatDiagnosticErrorMessage(error: unknown): string {
const message = error instanceof Error ? error.message : String(error);
return message.includes('Failed to fetch') ? `${message} (check CORS?)` : message;
}
export function extractJsonRpcMethods(body: BodyInit | null | undefined): string[] | undefined {
if (typeof body !== 'string') {
return undefined;
}
try {
const parsed = JSON.parse(body);
const messages = Array.isArray(parsed) ? parsed : [parsed];
const methods = messages
.map((message: Record<string, unknown>) =>
typeof message?.method === 'string' ? (message.method as string) : undefined
)
.filter((method: string | undefined): method is string => Boolean(method));
return methods.length > 0 ? methods : undefined;
} catch {
return undefined;
}
}
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