LLMs

Jail-Breaked & Offline Appliances: It’s becoming increasingly clear that we’ll be able to interact with everyday appliances and devices with natural language. As locally run LLMs become more efficient and powerful, the prospects of having a conversation with your coffee machine in the morning aren’t unreasonable. After all, who wants to tinker with... See more

Why is Discord such a good GTM for AI applications?

Text interface. Most users are just generating images, videos, and audio in these Discord servers. Prompts are easily expressible in simple text commands. It’s why we’ve seen image generation strategies like Midjourney (all-in-one) flourish in Discord while more raw diffusion models haven’t grown... See more

The quality of dataset is 95% of everything. The rest 5% is not to ruin it with bad parameters.

A core research interest of mine is imagining new kinds of interfaces to text documents that are made possible by modern AI and software. I think an interesting place to look for such ideas may be interface designs for reading and writing legal documents .

Legal document-wrangling tools have a handful of properties that make it fertile ground for... See more

GPT-4 Turbo can accept images as inputs in the Chat Completions API, enabling use cases such as generating captions, analyzing real world images in detail, and reading documents with figures. For example, BeMyEyes uses this technology to help people who are blind or have low vision with daily tasks like identifying a product or navigating a store.... See more

We consider these aspects of our problem:

• Latency : How fast does the system need to respond to user input?


• Task Complexity : What level of understanding is required from the LLM? Is the input context and prompt super domain-specific?


• Prompt Length : How much context needs to be provided for the LLM to do its task?


• Quality : What is the acceptable

... See more

Fine-Tuning for LLM Research by AI Hero

This repo contains the code that will be run inside the container. Alternatively, this code can also be run natively. The container is built and pushed to the repo using Github actions (see below). You can launch the fine tuning job using the examples in the https://github.com/ai-hero/llm-research-examples... See more

Giskard is a Python library that automatically detects vulnerabilities of AI models, from tabular models to LLM, including: performance biases, data leakage, spurious correlation, hallucination, toxicity, security issues and many more.

It's a powerful tool that helps data scientists save time and effort drilling down on model issues, and produce... See more

One interesting thing about LLMs is that they can actually recover (and without error loops). You can have a step that doesn't work right, and a later step can use its common-sense knowledge to ignore some of the missing results, conflicting information, etc. One of the problems with developing with LLMs is that the machine will often cover up... See more