vwxyzjn

from collections import defaultdict
import random
from huggingface_hub import hf_hub_download
from datasets import Dataset
import numpy as np
import pandas as pd
from transformers import AutoTokenizer
from rich.console import Console
from rich.table import Table
from trl import DPOTrainer

ChrisHayduk

"""

The code below combines approaches published by both @eugene-yh and @jinyongyoo on Github. 

Thanks for the contributions guys!

"""

import torch
import peft

rain-1

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

Helw150

from transformers import AutoTokenizer, T5ForConditionalGeneration

# Model Init
n_gpu = 8
tokenizer = AutoTokenizer.from_pretrained("google/flan-ul2")
model = T5ForConditionalGeneration.from_pretrained("google/flan-ul2")
heads_per_gpu = len(model.encoder.block) // n_gpu 
device_map = {
    gpu: list(
        range(

karpathy

"""
stable diffusion dreaming
creates hypnotic moving videos by smoothly walking randomly through the sample space

example way to run this script:

$ python stablediffusionwalk.py --prompt "blueberry spaghetti" --name blueberry

to stitch together the images, e.g.:
$ ffmpeg -r 10 -f image2 -s 512x512 -i blueberry/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p blueberry.mp4

breandan

" To only apply these macros in e.g. Python files, prepend 'autocmd FileType py', e.g.:
" autocmd FileType py inoremap \mu μ

inoremap \alpha α
inoremap \beta β
inoremap \gamma γ
inoremap \delta δ
inoremap \epsilon ε
inoremap \zeta ζ
inoremap \eta η

tangentstorm

NB. Code from the "Basic Animation In J" video
NB. https://www.youtube.com/watch?v=uL-70fMTVnw
NB. ------------------------------------------------------------------------

NB. animation demo

load 'viewmat'
coinsert'jgl2'

wd 'pc w0 closeok'                  NB. parent control (window) named 'w0'