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RainWarrior

{
  "parameters":  { "name": <time_value>, ... },
  "clips":       { "name": <clip>, ... },
  "states":      [ "name", ... ],
  "transitions": { "name_from": "name_to", ... },
  "start_state": "name"
}

<time_value> ::=
  <number>                                    // result = number; Constant value.

kabachuha

package artem226.quadratic.main;

import java.io.File;
import java.io.IOException;
import java.util.Random;
import java.util.Scanner;

import org.apache.commons.io.FileUtils;

import com.google.gson.Gson;

kabachuha

#include <iostream>

using namespace std;

int main()
{
	const int size = 10;
	float a[size];
	
	for(int i = 0; i < size; i++)

Integralist

import asyncio

from functools import wraps


def dec(fn):
    @wraps(fn)
    async def wrapper(*args, **kwargs):
        print(fn, args, kwargs)  # <function foo at 0x10952d598> () {}
        await asyncio.sleep(5)

vadimkantorov

# ported from https://github.com/pvigier/perlin-numpy/blob/master/perlin2d.py

import torch
import math

def rand_perlin_2d(shape, res, fade = lambda t: 6*t**5 - 15*t**4 + 10*t**3):
    delta = (res[0] / shape[0], res[1] / shape[1])
    d = (shape[0] // res[0], shape[1] // res[1])
    
    grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1])), dim = -1) % 1

Birch-san

Install HF Code Autocomplete VSCode plugin.

We are not going to set an API token. We are going to specify an API endpoint.
We will try to deploy that API ourselves, to use our own GPU to provide the code assistance.

We will use bigcode/starcoder, a 15.5B param model.
We will use NF4 4-bit quantization to fit this into 10787MiB VRAM.
It would require 23767MiB VRAM unquantized. (still fits on a 4090, which has 24564MiB)!

Setup API