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@veekaybee
veekaybee / normcore-llm.md
Last active July 28, 2024 18:55
Normcore LLM Reads

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

Screenshot 2023-12-18 at 10 40 27 PM

Pre-Transformer Models

@theycallmeloki
theycallmeloki / Raspberry Pi 4 x 6 cluster 65B Llama runner gist
Last active May 28, 2024 11:43
an ansbile script that quickly bootstraps a Pi 4 cluster with tools required to run a 65B llama model
You will need the following two files, be sure to change the IP address in both sections, the `hostfile_contents`
as well as the ansible inventory section to reflect your configuration
Rest of the steps should run parallely across the different machines, for any updates to this script,
check this thread: https://github.com/ggerganov/llama.cpp/issues/2164#issuecomment-1639640846
If you want an automated way to do this in the future and would like an opionated setup procedure,
check this repo: https://github.com/theycallmeloki/edith-cli
@andy-thomason
andy-thomason / Genomics_A_Programmers_Guide.md
Created May 14, 2019 13:32
Genomics a programmers introduction

Genomics - A programmer's guide.

Andy Thomason is a Senior Programmer at Genomics PLC. He has been witing graphics systems, games and compilers since the '70s and specialises in code performance.

https://www.genomicsplc.com

@npearce
npearce / install-docker.md
Last active July 27, 2024 07:34
Amazon Linux 2 - install docker & docker-compose using 'sudo amazon-linux-extras' command

UPDATE (March 2020, thanks @ic): I don't know the exact AMI version but yum install docker now works on the latest Amazon Linux 2. The instructions below may still be relevant depending on the vintage AMI you are using.

Amazon changed the install in Linux 2. One no-longer using 'yum' See: https://aws.amazon.com/amazon-linux-2/release-notes/

Docker CE Install

sudo amazon-linux-extras install docker
sudo service docker start
@druska
druska / engine.c
Created September 17, 2018 15:18
Quant Cup 1's winning order book implementation
/*****************************************************************************
* QuantCup 1: Price-Time Matching Engine
*
* Submitted by: voyager
*
* Design Overview:
* In this implementation, the limit order book is represented using
* a flat linear array (pricePoints), indexed by the numeric price value.
* Each entry in this array corresponds to a specific price point and holds
* an instance of struct pricePoint. This data structure maintains a list
@giacobenin
giacobenin / udp2tcp.py
Last active January 20, 2021 05:21 — forked from vxgmichel/udpproxy.py
UDP to TCP 1-Way Relay using asyncio
"""
1-way UDP to TCP relay.
Test with netcat
1) Run TCP server:
nc -l 999
2) Run UDP proxy:
python udpproxy.py
3) Run UDP client:
nc -u 127.0.0.1 8888

Quick Tips for Fast Code on the JVM

I was talking to a coworker recently about general techniques that almost always form the core of any effort to write very fast, down-to-the-metal hot path code on the JVM, and they pointed out that there really isn't a particularly good place to go for this information. It occurred to me that, really, I had more or less picked up all of it by word of mouth and experience, and there just aren't any good reference sources on the topic. So… here's my word of mouth.

This is by no means a comprehensive gist. It's also important to understand that the techniques that I outline in here are not 100% absolute either. Performance on the JVM is an incredibly complicated subject, and while there are rules that almost always hold true, the "almost" remains very salient. Also, for many or even most applications, there will be other techniques that I'm not mentioning which will have a greater impact. JMH, Java Flight Recorder, and a good profiler are your very best friend! Mea

@simonw
simonw / recover_source_code.md
Last active June 21, 2024 00:11
How to recover lost Python source code if it's still resident in-memory

How to recover lost Python source code if it's still resident in-memory

I screwed up using git ("git checkout --" on the wrong file) and managed to delete the code I had just written... but it was still running in a process in a docker container. Here's how I got it back, using https://pypi.python.org/pypi/pyrasite/ and https://pypi.python.org/pypi/uncompyle6

Attach a shell to the docker container

Install GDB (needed by pyrasite)

apt-get update && apt-get install gdb
@vxgmichel
vxgmichel / udpproxy.py
Created February 2, 2017 10:05
UDP proxy server using asyncio
"""UDP proxy server."""
import asyncio
class ProxyDatagramProtocol(asyncio.DatagramProtocol):
def __init__(self, remote_address):
self.remote_address = remote_address
self.remotes = {}

Principles of Adult Behavior

  1. Be patient. No matter what.
  2. Don’t badmouth: Assign responsibility, not blame. Say nothing of another you wouldn’t say to him.
  3. Never assume the motives of others are, to them, less noble than yours are to you.
  4. Expand your sense of the possible.
  5. Don’t trouble yourself with matters you truly cannot change.
  6. Expect no more of anyone than you can deliver yourself.
  7. Tolerate ambiguity.
  8. Laugh at yourself frequently.