LLMs

LLM-PowerHouse: A Curated Guide for Large Language Models with Custom Training and Inferencing

Welcome to LLM-PowerHouse, your ultimate resource for unleashing the full potential of Large Language Models (LLMs) with custom training and inferencing. This GitHub repository is a comprehensive and curated guide designed to empower developers,... See more

ghimiresunil • GitHub - ghimiresunil/LLM-PowerHouse-A-Curated-Guide-for-Large-Language-Models-with-Custom-Training-and-Inferencing: LLM-PowerHouse: Unleash LLMs' potential through curated tutorials, best practices, and ready-to-use code for custom training and inferencing.

Take a look at our official page for user documentation and examples: langtest.org

Key Features

Generate and execute more than 50 distinct types of tests only with 1 line of code

Test all aspects of model quality: robustness, bias, representation, fairness and accuracy.

Automatically augment training data based on test results (for select models)

GitHub - BrunoScaglione/langtest: Deliver safe & effective language models

How can we make interacting with conversational models feel more natural?

Every conversational interface to a language model adopts the same pattern:

A chat history sidebar, with each conversation lasting just a few turns

New sessions always begin in a brand-new thread

Every user query must always elicit exactly one response

None of these assumptions... See more

Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]

no reason to build any kind of software product these days that doesn't have a significant UX/domain knowledge component

Discord - A New Way to Chat with Friends & Communities

Traditional AI - The most secure, understandable, and performant. However, Good implementations of traditional AI require that we define the rules behind the system, which makes it unfeasible for many of the use cases that the other 2 techniques thrive on.

Supervised Machine Learning- Middle of the road b/w AI and Deep Learning. Good when we have

Devansh • How to Pick between Traditional AI, Supervised Machine Learning, and Deep Learning [Thoughts]

Where would I add generative AI? Generative AI has the ease of accessibility of traditional AI, where people think it is understandable, but it does not have that feature in itself. It also has the opaque and costly nature of DL. Many companies are at the moment rushing into developing things with generative AI without having any prior foundation in AI and any processes set up to manage it: data ops, devops, …

Traditional AI forces you to think about how something works, understand the system, and then define the rules for it. ML lets you use features and feature importance to shortcut some. Deep Learning allows you to brute force it. Generative AI allows you to brute force without any background in DL.

For the deployment side of things, we found that the performance of our training process was quite slow, especially when it gets into these large language models and when you train from scratch. MosaicML offers what's called programmatic optimization, which is not so much on the hardware side of things, but rather on the algorithmic side. Can you... See more

CB Insights • 2024 Tech Trends

Source: CB Insights Report

DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference

Table of Contents

Introduction

Key LLM Serving Techniques

Dynamic SplitFuse: A Novel Prompt and Generation Composition Strategy

Performance Evaluation

DeepSpeed-FastGen: Implementation and Usage

Try out DeepSpeed-FastGen

Acknowledgements

1. Introduction

Large... See more

microsoft • DeepSpeed-FastGen

Disruptive innovation comes in two flavors: (1) New-market disruption, where the company creates and claims a new segment in an existing market by catering to an underserved customer base, or (2) Low-end disruption, in which a company uses a low-cost business model to enter at the bottom of an existing market and claim a segment.

Copilots don’t... See more

Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]

You have access to a proprietary asset (like data) that others don’t have easy access to. In our “write job postings” example, perhaps you have a corpus of thousands of job postings including some outcome scores (as to how well they did). You could use this data to create better job postings. Others don’t have ready access to this data. Note: The

Dharmesh Shah • How To Build a Defensible A.I. Startup

Protecting LLM products:
(1) Is hard to bootstrap. This already hints to existing customers or you need to get a bunch of your customers to co-develop (insurance model → companies pooling their data to solve a problem they all have). This runs into a bunch of issues: competitive drive of the companies, data privacy and security.
(2) Reserved for existing companies. This is the co-pilot model.
(3) This might be the most sustainable one, but it is also the hardest one. I have not seen anything in that direction yet besides OpenAI.