LLMs

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

Overview

Loki is our open-source solution designed to automate the process of verifying factuality. It provides a comprehensive pipeline for dissecting long texts into individual claims, assessing their worthiness for verification, generating queries for evidence search, crawling for evidence, and ultimately verifying the claims. This tool is... See more

Memory Considerations

Since co-occurrence matrices are square, they grow exponential with the number of entities being embedded. For 50k entities and a 32-bit data format, a dense matrix will already be at 10GB. 100k entities puts it at 40GB.

If you are trying to embed even more entities than that or have limited RAM available, you may need to use a... See more

The way that most RLHF is done to date has the entire response from a language model get an associated score. To anyone with an RL background, this is disappointing, because it limits the ability for RL methods to make connections about the value of each sub-component of text. Futures have been pointed to where this multi-step optimization comes at... See more

Systems can be dynamic. Machine learning models are inherently limited because they are trained on static datasets, so their “knowledge” is fixed. Therefore, developers need to combine models with other components, such as search and retrieval, to incorporate timely data. In addition, training lets a model “see” the whole training set, so more

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Setting up the necessary machine learning infrastructure to run these big models is another challenge. We need a dedicated model server for running model inference (using frameworks like Triton oder vLLM), powerful GPUs to run everything robustly, and configurability in our servers to make sure they're high throughput and low latency. Tuning the... See more

The AI engineering framework

Marvin is a lightweight AI engineering framework for building natural language interfaces that are reliable, scalable, and easy to trust.

Sometimes the most challenging part of working with generative AI is remembering that it's not magic; it's software. It's new, it's nondeterministic, and it's incredibly powerful - but... See more

How do models represent style, and how can we more precisely extract and steer it?

A commonly requested feature in almost any LLM-based writing application is “I want the AI to respond in my style of writing,” or “I want the AI to adhere to this style guide.” Aside from costly and complicated multi-stage finetuning processes like Anthropic’s RL with... See more

Google Deepmind used similar idea to make LLMs faster in Accelerating Large Language Model Decoding with Speculative Sampling. Their algorithm uses a smaller draft model to make initial guesses and a larger primary model to validate them. If the draft often guesses right, operations become faster, reducing latency.

There are some people speculating... See more