TikkunCreation

Sam Altman: "I think if you have a smart person who has learned to do good research and has the right sort of mindset, it only takes about six months to make them, you know, take a smart physics researcher and make them into a productive AI researcher. So we don't have enough talent in the field yet, but it's coming soon. We have a program at open AI that does exactly this. And I'm astonished how well it works."

Types of roles at AI companies

• Software Engineer (not the focus of this Gist): Build customer-facing features, optimize applications for speed and scale, use AI APIs. Prompt engineering expertise is generally helpful, but AI experience beyond using the APIs or using ChatGPT like an expert is generally not needed. This Gist isn't aimed at this role.
• Machine Learning Engineer: Build pipelines for data management, model training, and model deployment, to improve models (not the focus of this Gist). And/or implement cutting-edge research papers (a focus of this Gist).
• Research Engineer (a *

rain-1

This worked on 14/May/23. The instructions will probably require updating in the future.

llama is a text prediction model similar to GPT-2, and the version of GPT-3 that has not been fine tuned yet. It is also possible to run fine tuned versions (like alpaca or vicuna with this. I think. Those versions are more focused on answering questions)

Note: I have been told that this does not support multiple GPUs. It can only use a single GPU.

It is possible to run LLama 13B with a 6GB graphics card now! (e.g. a RTX 2060). Thanks to the amazing work involved in llama.cpp. The latest change is CUDA/cuBLAS which allows you pick an arbitrary number of the transformer layers to be run on the GPU. This is perfect for low VRAM.

• Clone llama.cpp from git, I am on commit 08737ef720f0510c7ec2aa84d7f70c691073c35d.

kconner

macOS Internals

Understand your Mac and iPhone more deeply by tracing the evolution of Mac OS X from prelease to Swift. John Siracusa delivers the details.

Starting Points

How to use this gist

You've got two main options:

mlaprise

Training open-source LLMs on ChatGPT output is a really bad idea.

Everyone is now racing to create open-source alternatives to compete with GPT3.5/GPT4. A common shortcut used by some teams to bootstrap their effort is to fine-tune their model on ChatGPT output. I used to think it was a good idea and totally fair play to do this. Actually, I still think it’s fair play. OpenAI effectively distilled the entire web into its models. They are saying themself that they are using publicly accessible information (mostly). So distilling their model is, in effect, distilling the public open web, so small Term of Service details aside, I don’t see major ethical problems with that. Right? Well, it’s not entirely true and I realized now that, even when ignoring the ethical considerations, using their output is a really bad idea.

First of all, from a purely technical point of view, as @yoavgo is explaining it beautifully in his recent post, there is no way to align LLMs correctly without the RLHF component. I encourag

neubig

# NOTE:
# You can find an updated, more robust and feature-rich implementation
# in Zeno Build
# - Zeno Build: https://github.com/zeno-ml/zeno-build/
# - Implementation: https://github.com/zeno-ml/zeno-build/blob/main/zeno_build/models/providers/openai_utils.py

import openai
import asyncio
from typing import Any

redcpp

import openai
import pinecone
from sentence_transformers import SentenceTransformer

class GPTConversationManager:
    def __init__(self, api_key, pinecone_api_key, index_name):
        self.api_key = api_key
        openai.api_key = self.api_key
        self.conversation_history = []
        self.pinecone_api_key = pinecone_api_key

perkinsjr

import { getAuth, withClerkMiddleware } from "@clerk/nextjs/server";
import { NextResponse, NextFetchEvent } from "next/server";
import type { NextRequest } from "next/server";
import { Ratelimit } from "@upstash/ratelimit";
import { Redis } from "@upstash/redis";

// Add public paths for Clerk to handle.
const publicPaths = ["/", "/sign-in*", "/sign-up*", "/api/blocked"];

// set your rate limit.

alexanderson1993

"use client";
import * as React from "react";
import { Input } from "@/components/ui/Input";
import { Button } from "@/components/ui/Button";
import {
  AlertDialog,
  AlertDialogContent,
  AlertDialogHeader,
  AlertDialogTitle,
  AlertDialogDescription,

andrewchilds

javascript:function parseChatGPTData(data) { const mapping = data.mapping; const conversationTitle = data.title; const createDate = new Date(data.create_time * 1000).toISOString().slice(0, 10); const messagesArray = Object.values(mapping) .filter(node => node.message) .map(node => { const message = node.message; const sender = message.author.role === 'user' ? 'You' : 'Assistant'; const content = message.content.parts.join(''); const createTime = message.create_time; return { sender: sender, content: content, createTime: createTime, }; }); messagesArray.sort((a, b) => a.createTime - b.createTime); return { date: createDate, title: conversationTitle, messages: messagesArray.map(({ sender, content }) => ({ sender, content })), }; } function download(filename, text) { const element = document.createElement('a'); element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text)); element.setAttribute('download', filename); element.style.display = 'none'; document.body.appendChild(element); e

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.