rh-operator-bundle-bot

{
    "certified": false,
    "deleted": false,
    "docker_image_digest": "sha256:fe658b0927d9e38c2676c825a8db2684183090f06e307120706d5188204c6819",
    "docker_image_id": "sha256:1bb613b74789e7e20fcc8d3fcab67c0662e538d2a707095eb7cb50bb20a13fab",
    "image_id": "sha256:fe658b0927d9e38c2676c825a8db2684183090f06e307120706d5188204c6819",
    "parsed_data": {
        "architecture": "amd64",
        "created": "2025-02-11 20:43:27.210578432 +0000 UTC",
        "image_id": "sha256:fe658b0927d9e38c2676c825a8db2684183090f06e307120706d5188204c6819",

flukyrobot

import { definePreset } from 'primevue/theming';

export default definePreset({
    // Primitive color tokens
    primitive: {
        // Primary Bootstrap colors
        blue: {
            50: '#e3f2fd',
            100: '#bbdefb',
            200: '#90caf9',

raca159

Parameter	Description
use_qlora	Whether to use QLoRA (4-bit quantization) for training.
lora_r	LoRA rank controls how many parameters will be injected (4, 8, 16)
model_name	Either SmolLM2-135M-Instruct or SSmolLM2-360M-Instruct.
learning_rate	Learning rate values (1e-4, 2e-4, 4e-4).
dataset_size	Control the amount of samples (small or medium)
max_steps	Number of training steps - fixed as 350 steps.
gradient_checkpointing	Whether to enable gradient checkpointing - fixed as True to optimize resource usage
tf32 & bf16	Controls mixed precision - fixed as True to optimize resource usage

torma616

--- comp.zsh	2025-02-10 22:36:57
+++ session_completion.zsh	2025-02-11 16:11:57
@@ -2,6 +2,17 @@
 
 autoload -U is-at-least
 
+_zellij_sessions() {
+    local line sessions desc; sessions=("${(@f)$(zellij ls -n)}")
+    local -a session_names_with_desc
+    for line in "${sessions[@]}"; do

raca159

model_name	LoRA r	dataset_size	quantized	learning_rate	final eval loss
SmolLM2-360M-Instruct	4	small	false	0.0005	0.16702
SmolLM2-360M-Instruct	8	small	false	0.0005	0.17016
SmolLM2-360M-Instruct	4	small	true	0.0005	0.17071
SmolLM2-360M-Instruct	8	small	true	0.0005	0.17121
SmolLM2-135M-Instruct	4	medium	false	0.0005	0.17130
SmolLM2-360M-Instruct	4	small	true	0.0002	0.17333
SmolLM2-360M-Instruct	4	small	false	0.0002	0.17467
SmolLM2-360M-Instruct	8	small	false	0.0002	0.18794

raca159

model_name	LoRA r	dataset_size	quantized	learning_rate	final eval loss
SmolLM2-135M-Instruct	4	medium	false	0.0005	0.17130
SmolLM2-135M-Instruct	8	medium	false	0.0005	0.18854
SmolLM2-135M-Instruct	4	small	false	0.0005	0.19940
SmolLM2-135M-Instruct	8	small	false	0.0005	0.21265

NotMorningYet

using System;
using System.ComponentModel.Design;
using System.Runtime.CompilerServices;
using System.Security.Cryptography;

namespace HW2_3_5
{
    internal class Program
    {

iamakanshab

Docker and Kubernetes Storage Optimization Guide for ML Testing

Table of Contents

• Image Optimization
• Runtime Storage Management
• Kubernetes Configuration
• Monitoring and Maintenance

Image Optimization

Ahkonsu

<?php

/*
Plugin Name: Gravity Forms Encryptorator
Plugin URI: https://github.com/humanmade/Gravity-Forms-Encryptorator
Description: Encrypt all Gravity Forms data on the way into the database using openssl public encryption, data can only be decrypted with the associated private key.
Author: Human Made Limited
Version: 1.0 Alpha
Author URI: http://www.hmn.md/
*/

liza-halp

#include <iostream>
using namespace std;

int main() {
    //создаю двухмерный массив
    int const ROWS = 3;
    int const COLS = 3;
    
    int arr [ROWS][COLS];