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:
// I'm tired of extensions that automatically: | |
// - show welcome pages / walkthroughs | |
// - show release notes | |
// - send telemetry | |
// - recommend things | |
// | |
// This disables all of that stuff. | |
// If you have more config, leave a comment so I can add it!! | |
{ |
#!/usr/bin/env python | |
import math | |
import matplotlib.pyplot as plt | |
import torch | |
import torch.nn as nn | |
from sklearn.datasets import make_moons | |
from torch import Tensor | |
from tqdm import tqdm |
import torch | |
import numpy | |
np = numpy | |
from geomloss import SamplesLoss # See also ImagesLoss, VolumesLoss | |
# preferences, need to be converted to costs | |
# row i = cost of moving each item from c to place i | |
# making cost non-negative will not changes solution matrix P | |
preference = numpy.asarray([[2, 2, 1 , 0 ,0], |