ArthurZucker

from transformers import AutoModelForCausalLM, AutoTokenizer, StaticCache
import torch
from typing import Optional
device = "cuda"

# Copied from the gpt-fast repo
def multinomial_sample_one_no_sync(probs_sort): # Does multinomial sampling without a cuda synchronization
    q = torch.empty_like(probs_sort).exponential_(1)
    return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)

Miffyli

# A very useful script for giving credit where it is due: by replacing (most) variable names by the authors who wrote them.
# Requirements: pip install gitpython
# Usage: python3 assign-credits.py <input_file> <output_file>
# Example: python3 assign-credits.py ./src/main.py ./src/main-credited.py

# NOTE that this is an awful idea with an awful implementation. The "generated" code likely does not work
# (e.g. typing stuff is skipped, class attribute names are replaced). The "author" is decided by the current HEAD of git repo, and whoever
# defines the variable first will get the credits (hahaa dunno if even this is right).

# It is getting late and I am tired of typing so I will let Github Copilot write something for me.

skye

# ==== Non-communicating processes

# 4x 1 chip per process:
os.environ["TPU_CHIPS_PER_PROCESS_BOUNDS"] = "1,1,1"
os.environ["TPU_PROCESS_BOUNDS"] = "1,1,1"
# Different per process:
os.environ["TPU_VISIBLE_DEVICES"] = "0" # "1", "2", "3"
# Pick a unique port per process
os.environ["TPU_MESH_CONTROLLER_ADDRESS"] = "localhost:8476"
os.environ["TPU_MESH_CONTROLLER_PORT"] = "8476"

thomwolf

model.zero_grad()                                   # Reset gradients tensors
for i, (inputs, labels) in enumerate(training_set):
    predictions = model(inputs)                     # Forward pass
    loss = loss_function(predictions, labels)       # Compute loss function
    loss = loss / accumulation_steps                # Normalize our loss (if averaged)
    loss.backward()                                 # Backward pass
    if (i+1) % accumulation_steps == 0:             # Wait for several backward steps
        optimizer.step()                            # Now we can do an optimizer step
        model.zero_grad()                           # Reset gradients tensors
        if (i+1) % evaluation_steps == 0:           # Evaluate the model when we...

jagrosh

Step 1 - Make a Discord Webhook

1. Find the Discord channel in which you would like to send commits and other updates

2. In the settings for that channel, find the Webhooks option and create a new webhook. Note: Do NOT give this URL out to the public. Anyone or service can post messages to this channel, without even needing to be in the server. Keep it safe!

Step 2 - Set up the webhook on Github

1. Navigate to your repository on Github, and open the Settings

TheGU

package main

import (
	"encoding/json"
	"fmt"
	"io/ioutil"
	"log"
	"net/http"
	"net/url"
	"os"

aolde

var http = require("http"),
    url = require("url"),
    path = require("path"),
    fs = require("fs")
    port = process.argv[2] || 8888,
    mimeTypes = {
      "html": "text/html",
      "jpeg": "image/jpeg",
      "jpg": "image/jpeg",
      "png": "image/png",