LLMs
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.
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)
- Sup
GitHub - BrunoScaglione/langtest: Deliver safe & effective language models
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]
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.
CB Insights • 2024 Tech Trends
Source: CB Insights Report
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
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.