LLMs

Developers are diving into the open source LLaMA, but what benefits does it have over GPT? We spoke to Sebastian Raschka from Lightning AI.

List Of All The Websites Companies like Google, FB, OpenAI, Microsoft, etc, uses To Train Large Language Models like ChatGPT, Bard, Llama, etc.

IntroductionI feel that the world of coding is changing. In a couple of years, I expect a lot of new developer tools based on LLMs to come up. They will like...

WorLLMs. GitHub Gist: instantly share code, notes, and snippets.

I haven’t spent much time playing around with the latest LLMs, and decided to spend some time doing so. I was particularly curious about the usecase of using embeddings to supplement user prompts with additional, relevant data (e.g. supply the current status of their recent tickets into the prompt where they might inquire about progress on said tickets). This usecase is interesting because it’s very attainable for existing companies and products to take advantage of, and I imagine it’s roughly how e.

While there’s been a truly remarkable advance in large language models as they continue to scale up, facilitated by being trained and run on larger and larger GPU clusters, there is still a need to be able to run smaller models on devices that have constraints on memory and processing power. Being able to run models at the edge enables creating applications that may be more sensitive to user privacy or latency considerations - ensuring that user data does not leave the device.

Pseudocode is a fantastic way to sketch programs using informal, natural language, without worrying about specific syntax. It’s like…

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“What if -- despite all the hype -- we are in fact underestimating the effect LLMs will have on the nature of software distribution and end-user programming? some early, v tentative thoughts: 1/”

This is a pretty exciting moment in tech Like clockwork every decade or so since the broad adoption of electricity there’s been a new technical innovation that completely upends society once it beco

I’ve been thinking about a fruitful way to frame the act of writing code in the age of Copilot/Codex, and I don’t think “autocomplete on steriods” is it. Prompt-driven programming with an LLM is better thought of as a compiler. Just like what we understand as a compiler today translates from a high-level programming language like C++ or Java to machine code (actually, assembly language), you could view an LLM as a compiler that translates from English to a high-level language.

Though recent advances have been made in large language model (LLM) reasoning, LLMs still have a hard time with hierarchical multi-step reasoning tasks like developing sophisticated programs. Human programmers, in contrast to other token generators, have (usually) learned to break down difficult tasks into manageable components that work alone (modular) and work together (compositional). As a bonus, if human-generated tokens cause problems with a function, it should be possible to rewrite that part of the software without affecting the rest of the application. In contrast, it is naively anticipated that code LLMs will produce token sequences free from errors. This

Google's new technology, Confident Adaptive Language Modeling (CALM), speeds up large language models (LLM) upto 3 times.