On every machine in the cluster install openmpi and mlx-lm:
conda install conda-forge::openmpi
pip install -U mlx-lmNext download the pipeline parallel run script. Download it to the same path on every machine:
On every machine in the cluster install openmpi and mlx-lm:
conda install conda-forge::openmpi
pip install -U mlx-lmNext download the pipeline parallel run script. Download it to the same path on every machine:
It turns out if you're just doing inference, Llama can be written very concisely. This implementation includes paged attention. Speculative decoding can also be added for another speed boost however it's quite verbose and was left out to keep the implementation cleaner.
Download the Llama files and place them in a directory ./Llama3.2-3B (or whatever flavor of Llama you want).
Your directory structure should look like:
./Llama3.2-3B/consolidated.00.pth
hi, i'm daniel. i'm a 15-year-old with some programming experience and i do a little bug hunting in my free time. here's the insane story of how I found a single bug that affected over half of all Fortune 500 companies:
If you've spent some time online, you’ve probably come across Zendesk.
Zendesk is a customer service tool used by some of the world’s top companies. It’s easy to set up: you link it to your company’s support email (like support@company.com), and Zendesk starts managing incoming emails and creating tickets. You can handle these tickets yourself or have a support team do it for you. Zendesk is a billion-dollar company, trusted by big names like Cloudflare.
Personally, I’ve always found it surprising that these massive companies, worth billions, rely on third-party tools like Zendesk instead of building their own in-house ticketing systems.
| #!/usr/bin/env bash | |
| ColorOff='\033[0m' # Text Reset | |
| Yellow='\033[0;33m' # Yellow for important info | |
| Red='\033[0;31m' # Red for errors | |
| function infoMessage() { | |
| echo -e ${Yellow} | |
| echo $1 | |
| echo -e ${ColorOff} | |
| } |
Over the weekend, I decided to try running a Postgres database in my Homelab. In my current setup, the most convenient option for storage is NFS. However, NFS is especially tricky for databases. A misconfigured setup can lead to performance or data corruption issues.
After watching
| (function (global, factory) { | |
| typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory(require('path'), require('fs')) : | |
| typeof define === 'function' && define.amd ? define(['path', 'fs'], factory) : | |
| (global = typeof globalThis !== 'undefined' ? globalThis : global || self, global.alignmentgame = factory(global.require$$0, global.require$$1)); | |
| })(this, (function (require$$0, require$$1) { 'use strict'; | |
| var src = {}; | |
| // Not trying to hack you. This is the base64 of gltk.wasm.js (an LP solver), which you can verify by converting https://github.com/jvail/glpk.js/blob/master/dist/glpk.wasm to base64. | |
| // Since googlescript doesn't allow synchronous loading of WASM modules, I need to include it here manually. |
See the new site: https://postgresisenough.dev
FROM https://www.reddit.com/r/Fedora/comments/zmvkdj/8bitdo_ultimate_bluetooth_controller_working_in/
I've bought this new controller from 8BitDo and wished to use on linux, to my sadness the controller didn't work out of the box, neither by cable, the 2.4G dongle or bluetooth.
So I've tried a number of solutions and this one from u/GodOfEmus over in the 8bitdo community was the one to work for me:
Presented by Evadne Wu at Code BEAM Lite in Stockholm, Sweden on 12 May 2023
We have celebrated 10 years of Elixir and also nearly 25 years of Erlang since the open source release in December 1998.
Most of the libraries that were needed to make the ecosystem viable have been built, talks given, books written, conferences held and training sessions provided. A new generation of companies have been built on top of the Elixir / Erlang ecosystem. In all measures, we have achieved further reach and maturity than 5 years ago.