Tal Shiri tals

REPO HERE: https://github.com/tandpfun/wardrobe

name: extract-clothing-cutouts description: Extract high-quality, deduplicated transparent ecommerce clothing cutouts from a folder of photographs where people wear one or more garments. Use when Codex must find outfit or model photos, identify unique clothing across images, create focused references, reconstruct complete garments with Imagegen, remove a solid chroma background into RGBA PNGs, and output only the finished clothing images into a new folder under the current working directory.

Extract Clothing Cutouts

Turn photographs of worn clothing into source-faithful standalone catalog PNGs. Treat each result as a reconstruction from visible evidence, not literal segmentation whenever the wearer or another layer occludes part of the garment.

Reinforcement Learning for Language Models

Yoav Goldberg, April 2023.

Why RL?

With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much

Simple Hexdump

A simple hexdump module for Python.

Installation

The preferred installation method is:

pip install simple-hexdump

Hello,

When you look at a repo, be sure to use git log --all and look at the latest commits across all branches. You would see that my work is on the "dec6" branch, not the main branch.

In general, it's more reliable to DM me on twitter than to email me. I'll usually respond within a day. Emails are hit-or-miss.

Here is the critical difference:

High-Performance Matrix Multiplication

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Intro

Matrix multiplication is a mathematical operation that defines the product of

OS X Screencast to animated GIF

This gist shows how to create a GIF screencast using only free OS X tools: QuickTime, ffmpeg, and gifsicle.

Instructions

To capture the video (filesize: 19MB), using the free "QuickTime Player" application:

	/*
	* @file FObjectPool.h
	* @author David Gallardo Moreno
	*/

	#ifndef __SBX_OBJECT_POOL_H__
	#define __SBX_OBJECT_POOL_H__

	#include <Library/FRawPool.h>

	'''
	INSTALLING

	curl --create-dirs -o ~/.lldb/cfdata.py https://gist.githubusercontent.com/ikonst/364af37c44e5f549b722/raw/cfdata.py \
	&& echo 'command script import ~/.lldb/cfdata.py' >> ~/.lldbinit

	USING

	(lldb) cfdata_save some_cfdata /Users/john/foo
	(lldb) cfdata_save some_nsdata /Users/john/bar

	Pretty print tables summarizing properties of tensor arrays in numpy, pytorch, jax, etc.

	Now on pip! `pip install arrgh` https://github.com/nmwsharp/arrgh