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Dan Nguyen dannguyen

havin a normal one
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rain-1 /
Last active February 22, 2024 05:52
How to run Llama 13B with a 6GB graphics card

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.
simonw / deepest.sql
Created October 6, 2019 17:15
Query a twitter-to-sqlite database and find the "deepest" tweets in a reply thread, adapted from
with recursive thread as (
select id, in_reply_to_status_id, 0 as depth
from tweets
where in_reply_to_status_id is null
select, tweets.in_reply_to_status_id, 1 + thread.depth as depth
from thread join tweets on tweets.in_reply_to_status_id =
select * from thread order by depth desc
yossorion /
Last active April 7, 2024 22:55
What I Wish I'd Known About Equity Before Joining A Unicorn

What I Wish I'd Known About Equity Before Joining A Unicorn

Disclaimer: This piece is written anonymously. The names of a few particular companies are mentioned, but as common examples only.

This is a short write-up on things that I wish I'd known and considered before joining a private company (aka startup, aka unicorn in some cases). I'm not trying to make the case that you should never join a private company, but the power imbalance between founder and employee is extreme, and that potential candidates would

This document has moved!

It's now here, in The Programmer's Compendium. The content is the same as before, but being part of the compendium means that it's actively maintained.

bearfrieze /
Last active December 23, 2023 22:49
Comprehensions in Python the Jedi way

Comprehensions in Python the Jedi way

by Bjørn Friese

Beautiful is better than ugly. Explicit is better than implicit.

-- The Zen of Python

I frequently deal with collections of things in the programs I write. Collections of droids, jedis, planets, lightsabers, starfighters, etc. When programming in Python, these collections of things are usually represented as lists, sets and dictionaries. Oftentimes, what I want to do with collections is to transform them in various ways. Comprehensions is a powerful syntax for doing just that. I use them extensively, and it's one of the things that keep me coming back to Python. Let me show you a few examples of the incredible usefulness of comprehensions.

toddwschneider /
Created December 1, 2015 12:28
How many NYC taxi trips are uniquely identifiable by census tracts and the hour of pickup time

40% of NYC Taxi Trips are Uniquely Identified by Pickup/Drop Off Census Tracts and Hour

In my recent post analyzing 1.1 billion NYC taxi and Uber trips, I included a section about privacy concerns which showed how precise latitude/longitude coordinates of taxi pickups and drop offs could potentially be used to reveal personal information about where people live, work, socialize, etc.

I wrote that if the Taxi & Limousine Commission wanted to avoid disclosing personal information, they would have to remove latitude/longitude from the dataset, perhaps replacing them with coarser census tract location data. Now it seems like maybe census tracts are still too precise.

I hadn't previously investigated how well census tracts uniquely identify pickups and drop offs, but **it turns out that if you

paulirish /
Last active April 10, 2024 16:18
What forces layout/reflow. The comprehensive list.

What forces layout / reflow

All of the below properties or methods, when requested/called in JavaScript, will trigger the browser to synchronously calculate the style and layout*. This is also called reflow or layout thrashing, and is common performance bottleneck.

Generally, all APIs that synchronously provide layout metrics will trigger forced reflow / layout. Read on for additional cases and details.

Element APIs

Getting box metrics
  • elem.offsetLeft, elem.offsetTop, elem.offsetWidth, elem.offsetHeight, elem.offsetParent
pixeltrix /
Last active February 18, 2024 19:20
When should you use DateTime and when should you use Time?

When should you use DateTime and when should you use Time?

It's a common misconception that [William Shakespeare][1] and [Miguel de Cervantes][2] died on the same day in history - so much so that UNESCO named April 23 as [World Book Day because of this fact][3]. However because England hadn't yet adopted [Gregorian Calendar Reform][4] (and wouldn't until [1752][5]) their deaths are actually 10 days apart. Since Ruby's Time class implements a [proleptic Gregorian calendar][6] and has no concept of calendar reform then there's no way to express this. This is where DateTime steps in:

>> shakespeare = DateTime.iso8601('1616-04-23', Date::ENGLAND)
=> Tue, 23 Apr 1616 00:00:00 +0000
>> cervantes = DateTime.iso8601('1616-04-23', Date::ITALY)
=> Sat, 23 Apr 1616 00:00:00 +0000
chrismdp /
Last active March 5, 2024 12:57
Uploading to S3 in 18 lines of Shell (used to upload builds for
# You don't need Fog in Ruby or some other library to upload to S3 -- shell works perfectly fine
# This is how I upload my new Sol Trader builds (
# Based on a modified script from here:
S3KEY="my aws key"
S3SECRET="my aws secret" # pass these in
function putS3
hadley /
Created March 13, 2015 18:49
My advise on what you need to do to become a data scientist...

If you were to give recommendations to your "little brother/sister" on things that they need to do to become a data scientist, what would those things be?

I think the "Data Science Venn Diagram" ( is a great place to start. You need three things to be a good data scientist:

  • Statistical knowledge
  • Programming/hacking skills
  • Domain expertise

Statistical knowledge