MaTriXy

import asyncio
import os
import sys
from dataclasses import dataclass

import aiohttp
import google.ai.generativelanguage as glm
from dotenv import load_dotenv
from loguru import logger
from runner import configure

MaTriXy

# train_grpo.py
import re
import torch
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig
from trl import GRPOConfig, GRPOTrainer

# Load and prep dataset

MaTriXy

from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from pydantic import BaseModel
from browser_use import ActionResult, Agent, Controller
from browser_use.browser.context import BrowserContext
from browser_use.browser.browser import Browser, BrowserConfig
import asyncio
import os
import json
import re

MaTriXy

MaTriXy

<?

	//
	// AUTO KEYWORD-BASED FOLLOWER CURATION BOT (by @levelsio)
	//
	// File: twitterFollowerCuratorBot.php
	//
	// Created: May 2021
	// License: MIT
	//

MaTriXy

#!/bin/bash

# Check if a GitHub URL is provided as an argument
if [ -z "$1" ]; then
    echo "Usage: $0 <github_url>"
    exit 1
fi

# Store the GitHub URL
GIT_URL="$1"

MaTriXy

Detailed writeup: https://huggingface2.notion.site/How-to-split-Flux-transformer-and-run-inference-aa1583ad23ce47a78589a79bb9309ab0

But TLDR is we split the models where possible and decouple the different stages of pipeline

MaTriXy

"""
A minimal, fast example generating text with Llama 3.1 in MLX.

To run, install the requirements:
    
    pip install -U mlx transformers fire

Then generate text with:

    python l3min.py "How tall is K2?"

MaTriXy

import androidx.compose.foundation.layout.Box
import androidx.compose.runtime.Composable
import androidx.compose.ui.Modifier
import androidx.compose.ui.draw.drawWithContent
import androidx.compose.ui.geometry.Offset
import androidx.compose.ui.graphics.BlendMode
import androidx.compose.ui.graphics.Color
import androidx.compose.ui.graphics.nativeCanvas

@Composable

MaTriXy

@Composable
fun ParallaxScreen(modifier: Modifier = Modifier) {
    val context = LocalContext.current
    val scope = rememberCoroutineScope()

    var data by remember { mutableStateOf<SensorData?>(null) }

    DisposableEffect(Unit) {
        val dataManager = SensorDataManager(context)
        dataManager.init()