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)_/¯

wilsonowilson

{
  "workbench.colorTheme": "Aura Dark",
  "workbench.iconTheme": "material-icon-theme",
  "editor.fontFamily": "'Geist Mono', Menlo, Monaco, 'Courier New', monospace",
  "apc.listRow": {
    "height": 24,
    "fontSize": 11
  },
  "window.titleBarStyle": "native",
  "apc.font.family": "Geist Mono",

kamilogorek

adrienbrault

# Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

# Build it
make clean
LLAMA_METAL=1 make

# Download model
export MODEL=llama-2-13b-chat.ggmlv3.q4_0.bin

josephrocca

// See the comments at the end for a model that does much better than e5-large-v2 while being a third of the size.

let { pipeline } = await import('https://cdn.jsdelivr.net/npm/@xenova/[email protected]');
let extractor = await pipeline('feature-extraction', 'Xenova/e5-large-v2');

// Note: If you're just comparing "passages" with one another, then just prepend "passage: " to all texts. Only use "query: " if the text is a short "search query" like in the above example.
let passage1 = await extractor(`passage: The Shawshank Redemption is a true masterpiece of cinema.`, { pooling: 'mean', normalize: true });
let passage2 = await extractor(`passage: The film should not be exposed to sunlight when removing it from the wrapper. Otherwise your movie will come out bad.`, { pooling: 'mean', normalize: true });
let query = await extractor(`query: movie review`, { pooling: 'mean', normalize: true });

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.

Quasimondo

import torch

#USE AT YOUR OWN RISK

#local path to runwayML SD 1.5 checkpoint (https://huggingface.co/runwayml/stable-diffusion-v1-5)
ckpt_15 = "./v1-5-pruned-emaonly.ckpt"

#local path to StabilityAI finetuned autoencoder (https://huggingface.co/stabilityai/sd-vae-ft-mse)
ckpt_vae = "./vae-ft-mse-840000-ema-pruned.ckpt"

veqtor

# This is a sample Python script.

# Press ⌃R to execute it or replace it with your code.
# Press Double ⇧ to search everywhere for classes, files, tool windows, actions, and settings.

import aiohttp

event_loop = None
import asyncio
from pprint import pprint

ries9112

// add script to a button inside Cryptovoxels to return a response

feature.on('click',e=>{
  
// Use GraphQL endpoint to pull data and make model that takes x,y Decentraland coordinates and returns prediction based on the latest 10 sales!

fetch('https://api.thegraph.com/subgraphs/name/decentraland/marketplace', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',

ries9112

query findSecondarySales($address: String = "tz1c2iwyckUCcicx2qxqtwLEartYFEHg1pvB")  {
  hic_et_nunc_swap(where: {status: {_in: [1]}, token: {swaps: {trades: {buyer: {address: {_eq: $address}}}}}}, order_by: {price: desc}) {
    price
    status
    token {
      title
      mime
      description
      id
      artifact_uri