willccbb

# train_grpo.py
#
# See https://github.com/willccbb/verifiers for ongoing developments
#
import re
import torch
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig
from trl import GRPOConfig, GRPOTrainer

hamelsmu

def follow_user_follows(client, target_user):
    "Follow everyone the target_user is following."
    
    cursor = None
    total_followed = 0

    while True:
        # Step 1: Fetch a batch of accounts the target user is following
        # https://docs.bsky.app/docs/api/app-bsky-graph-get-follows
        response = client.app.bsky.graph.get_follows({

OrionReed

// 3D Dom viewer, copy-paste this into your console to visualise the DOM as a stack of solid blocks.
// You can also minify and save it as a bookmarklet (https://www.freecodecamp.org/news/what-are-bookmarklets/)
(() => {
  const SHOW_SIDES = false; // color sides of DOM nodes?
  const COLOR_SURFACE = true; // color tops of DOM nodes?
  const COLOR_RANDOM = false; // randomise color?
  const COLOR_HUE = 190; // hue in HSL (https://hslpicker.com)
  const MAX_ROTATION = 180; // set to 360 to rotate all the way round
  const THICKNESS = 20; // thickness of layers
  const DISTANCE = 10000; // ¯\\_(ツ)_/¯

sekstini

laksjdjf

'''
https://gist.github.com/kohya-ss/3f774da220df102548093a7abc8538ed
1. put this file in ComfyUI/custom_nodes
2. load node from <loaders>
'''

import torch
from comfy.ldm.modules.diffusionmodules.openaimodel import forward_timestep_embed, timestep_embedding, th

def apply_control(h, control, name):

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

rom1504

Install

git clone https://github.com/mlfoundations/open_clip.git
cd open_clip
python3.8 -m venv .env
source .env/bin/activate
pip install -U pip
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
pip install -e .

rahulrajpl

tjjfvi

; acceleration_enabled = {acceleration_enabled}
; acceleration_infill = {acceleration_infill}
; acceleration_ironing = {acceleration_ironing}
; acceleration_layer_0 = {acceleration_layer_0}
; acceleration_prime_tower = {acceleration_prime_tower}
; acceleration_print = {acceleration_print}
; acceleration_print_layer_0 = {acceleration_print_layer_0}
; acceleration_roofing = {acceleration_roofing}
; acceleration_skirt_brim = {acceleration_skirt_brim}
; acceleration_support = {acceleration_support}

gilrosenthal

!pip install fastai
!apt-get -qq install -y libsm6 libxext6 && pip install -q -U opencv-python
import cv2
from os import path
from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag
platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())

accelerator = 'cu80' if path.exists('/opt/bin/nvidia-smi') else 'cpu'

!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.3.0.post4-{platform}-linux_x86_64.whl torchvision