Nicolay Gerold

t on LLMs and SQL highlighting why they don’t consistently work :

• “LLMs can write SQL, but they are often prone to making up tables, making up field”


• “LLMs have some context window which limits the amount of text they can operate over”


• “The SQL it writes may be incorrect for whatever reason, or it could be correct but just return an unexpected

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Repository for the paper "The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning", including 1.84M CoT rationales extracted across 1,060 tasks"

Paper Link : https://arxiv.org/abs/2305.14045

• If you are looking to develop an AI application, and you have a Mac or Linux machine, Ollama is great because it’s very easy to set up, easy to work with, and fast.


• If you are looking to chat locally with documents, GPT4All is the best out of the box solution that is also easy to set up


• If you are looking for advanced control and insight into neural

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a couple of the top of my head:

• LLM in the loop with preference optimization


• synthetic data generation


• cross modality "distillation" / dictionary remapping


• constrained decoding

code was clean, organized, and logical . Each decision made in their code made sense, and when something didn’t, it was documented well within the code.

A good way to write clean code is to follow principles, like the SOLID principles. Though they were initially designed with OOP (object-oriented programming) in mind, they are extensible to general... See more

Code is for humans, not just for computers.

Code is for the engineers on your team , who read, maintain, and build on top of your code.

Code is for the users , whether it’s a kid on their phone, a developer calling your API, or yourself.

I really think that we could be entering a third epoch of computing. The microchip brought the marginal cost of compute to 0. The internet brought the marginal cost of distribution to 0. These large models actually bring the marginal cost of creation to 0.

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

Thanks to the cognitive architecture design, goal-oriented behavior is at the core of our LLM agents. When presented with a goal, they employ a planning and execution process to achieve it. If the goal can be accomplished using atomic game actions, we generate a plan that outlines how to solve the objective. For longer-term goals, we decompose the... See more