| // Updated: Aug. 20, 2024 | |
| // Run: node testRegex.js whatever.txt | |
| // Live demo: https://jina.ai/tokenizer | |
| // LICENSE: Apache-2.0 (https://www.apache.org/licenses/LICENSE-2.0) | |
| // COPYRIGHT: Jina AI | |
| const fs = require('fs'); | |
| const util = require('util'); | |
| // Define variables for magic numbers | |
| const MAX_HEADING_LENGTH = 7; |
| import gradio as gr | |
| from openai import OpenAI | |
| import jinja2 | |
| from transformers import AutoTokenizer | |
| # get an api key from hyperbolic. | |
| api_key = "..." | |
| # Initialize the OpenAI client | |
| client = OpenAI( |
| // | |
| // InputNotWorkingIndicator.swift | |
| // | |
| // Created by Drew Olbrich on 6/26/24. | |
| // Copyright © 2024 Lunar Skydiving LLC. All rights reserved. | |
| // | |
| // MIT License | |
| // | |
| // Permission is hereby granted, free of charge, to any person obtaining a copy | |
| // of this software and associated documentation files (the "Software"), to deal |
| import SwiftUI | |
| // Remember to download FontSettings.swift | |
| struct WWDC24AnimatedTextView: View { | |
| var text = "Hello, World!" | |
| var animation: Animation = .easeInOut | |
| var targetFontSize: CGFloat = 40 | |
| var minimumFontSize: CGFloat = 30 | |
| var targetFontWeight: Font.Weight = .semibold |
| import SwiftUI | |
| struct AnimatingMeshView: View { | |
| let referenceDate: Date | |
| var body: some View { | |
| TimelineView(.animation) { context in | |
| let t = context.date.timeIntervalSince(referenceDate) | |
| MeshGradient(width: 5, height: 4, points: [ |
| // | |
| // KeyboardToolbar.swift | |
| // | |
| // Created by James Sedlacek on 9/20/23. | |
| // | |
| import SwiftUI | |
| import Combine | |
| @Observable |
| # SHA256: 3e4bbd21756ae30c24ff7d6942656be024139f8180b7bddd4e5c62a9dfbd8c79 | |
| # Get the strings from the data section | |
| data_section = bv.get_section_by_name("__data") | |
| symbols = bv.get_symbols(data_section.start, (data_section.end-data_section.start)) | |
| xor_val = 0 | |
| xor=Transform['XOR'] | |
| # Find the _xor_val |
The screenshots were taken on different sessions.
The entire sessions are included on the screenshots.
I lost the original prompts, so I had to reconstruct them, and still managed to reproduce.
The "compressed" version is actually longer! Emojis and abbreviations use more tokens than common words.
| def lerp(a, b, t): | |
| return (b - a) * t + a | |
| def bias(i, x, beta): | |
| return 1 - jnp.asarray(beta, x.dtype) ** (i + 1) | |
| @optimizer | |
| def adam(step_size, b1=0.9, b2=0.999, eps=1e-8) -> OptimizerResult: | |
| """Construct optimizer triple for Adam. |
| # Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint. | |
| # *Only* converts the UNet, VAE, and Text Encoder. | |
| # Does not convert optimizer state or any other thing. | |
| # Written by jachiam | |
| import argparse | |
| import os.path as osp | |
| import torch |
