Develop an AI prompt that solves random 12-token instances of the A::B problem (defined here), with 90%+ success rate.
We'll use your prompt as the SYSTEM PROMPT, and a specific instance of problem as the PROMPT, inside XML tags. Example:
Develop an AI prompt that solves random 12-token instances of the A::B problem (defined here), with 90%+ success rate.
We'll use your prompt as the SYSTEM PROMPT, and a specific instance of problem as the PROMPT, inside XML tags. Example:
""" To use: install LLM studio (or Ollama), clone OpenVoice, run this script in the OpenVoice directory | |
git clone https://github.com/myshell-ai/OpenVoice | |
cd OpenVoice | |
git clone https://huggingface.co/myshell-ai/OpenVoice | |
cp -r OpenVoice/* . | |
pip install whisper pynput pyaudio | |
""" | |
from openai import OpenAI | |
import time |
Audience: I assume you heard of chatGPT, maybe played with it a little, and was imressed by it (or tried very hard not to be). And that you also heard that it is "a large language model". And maybe that it "solved natural language understanding". Here is a short personal perspective of my thoughts of this (and similar) models, and where we stand with respect to language understanding.
Around 2014-2017, right within the rise of neural-network based methods for NLP, I was giving a semi-academic-semi-popsci lecture, revolving around the story that achieving perfect language modeling is equivalent to being as intelligent as a human. Somewhere around the same time I was also asked in an academic panel "what would you do if you were given infinite compute and no need to worry about labour costs" to which I cockily responded "I would train a really huge language model, just to show that it doesn't solve everything!". We
Laptop (ThinkPad) Lenovo X1 Carbon 6th Gen (Type 20KH, 20KG)
If you are using your ThinkPad X1G6 with Linux in "Legacy only" mode, there's only one way how to update BIOS - you should use "BIOS Update (Bootable CD)". So this is a step by step guid how to do it.
fwupdmgr get-devices
var timer=100;document.querySelectorAll("div > input[type='checkbox']:checked").forEach((interest) => {setTimeout(function(){interest.click()},timer);timer+=2000;}); |
import argparse | |
import os | |
import fasttext | |
from sklearn.base import BaseEstimator | |
from sklearn.metrics import f1_score | |
from sklearn.model_selection import cross_val_score, StratifiedKFold | |
def read_data(data_dir): |
''' Script for downloading all GLUE data. | |
Note: for legal reasons, we are unable to host MRPC. | |
You can either use the version hosted by the SentEval team, which is already tokenized, | |
or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually. | |
For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example). | |
You should then rename and place specific files in a folder (see below for an example). | |
mkdir MRPC | |
cabextract MSRParaphraseCorpus.msi -d MRPC |
using Microsoft.Office.Interop.Word; | |
using System.IO; | |
namespace MSWordExample | |
{ | |
public class LineNumberingKiller | |
{ | |
static void Main(string[] args) | |
{ | |
Application word = new Application(); |
# If errors are found, do this | |
# clear contents of C:\Users\<username>\AppData\Local\Temp\gen_py | |
# that should fix it, to test it type | |
import win32com.client | |
app = win32com.client.gencache.EnsureDispatch('Word.Application') | |
app.Visible = True |