bitti

#!/usr/bin/ruby
require 'redcarpet'
require 'erb'

input = ARGV[0] && ARGV[0] != "-" &&  File.open(ARGV[0]) || STDIN
output = ARGV[1] && File.open(ARGV[1], "w") || STDOUT

File.join(ENV["XDG_CACHE_HOME"] || "#{ENV['HOME']}/.cache", "gfm-render")

class RenderWithTaskLists < Redcarpet::Render::HTML

nopeless

// Prime checking regex, which this code is based off of
// https://regex101.com/r/RIJkGF/1

type StringDivisible<n extends string, div extends string> = n extends `` ? true : n extends `${div}${infer r}` ? StringDivisible<r, div> : false;

type Substrings<n extends string, d extends string = ""> = n extends `${d}${infer r}` ? readonly [` ${d}`, ...Substrings<r, ` ${d}`>] : readonly [];
type StringFactors<n extends string> = Substrings<n> extends readonly [unknown, ...infer R extends readonly string[], unknown] ? R : never;
type StringIsPrime<n extends string, Factors extends readonly string[] = StringFactors<n>> = Factors extends readonly [infer s extends string, ...infer R extends readonly string[]] ? StringDivisible<n, s> extends true ? false : StringIsPrime<n, R> : true;

type RepeatStringByStringDigit<str extends string, d extends string> = 

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.

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models