Meta AI researchers trained a language model to fill in protein sequence gaps across millions of diverse proteins & scaled up to 15B parameters, creating the largest language model of proteins to date. More on our latest breakthrough in protein folding https://
bit.ly/3WoWcK2
LLMS
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Meta AI Creates Largest Protein Language Model with 15B Parameters
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Machine Learning GPT3 Website Free for Content Creators
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Machine learning for deep thought: a GPT3-based website that every content creator should jump on while it’s free… https://
qburst.co -
ERNIE-Layout Achieves SOTA Results on Document Understanding Tasks
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Introducing ERNIE-Layout, a layout knowledge enhanced document pre-training model that obtained SOTA results on 11 datasets and won 1st place on DocVQA (exceeding 90 for the 1st time) and WebSRC. Check Zero-shot Demo @huggingface
: http://
huggingface.co/spaces/PaddleP
addle/ERNIE-Layout
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Exploring Instruction Induction for Inferring AI Instruction Templates
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Wonder how well it fares for longer instructions — I played around a while ago with using instruction induction to infer “instruction templates” from examples and it seemed to work:
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Experimental Optimization of AI Instruction Prompts
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Cool extension of Honovich et al.’s “instruction induction”, experimentally optimizing to find the best instruction prompt for a given set of task examples: https://t.co/h4Rd2A9JwY
— Riley Goodside (@goodside) 4 novembre 2022Cool extension of Honovich et al.’s “instruction induction”, experimentally optimizing to find the best instruction prompt for a given set of task examples:
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Large Language Models Achieve Human-Level Prompt Engineering
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Large Language Models Are Human-Level Prompt Engineers Zhou et al.: https://
arxiv.org/abs/2211.01910 #MachineLearning #DeepLearning #ArtificialIntelligence -
Inverse Scaling Observed Up to 62B Model Parameters
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We also showed inverse scaling up to 62B on our model / prompt setup
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Powerful Tool for K-Shot Datasets and Prompt Chaining with Collapsible Tree UI
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@dust4ai takes some work to wrap your head around, but it's very powerful — gives a collapsible tree UI for representing k-shot example datasets, prompt templates, and prompt chaining with intermediate JS code. Replaces a lot of code around prompt APIs. http://
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Langchain Python Package Implements Advanced LLM Prompting Techniques
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@hwchase17
's Python package Langchain implements and lets you easily compose many published LLM prompting techniques. Implements self-asking, web search, REPL math, and several of my own prompts. -
EveryPrompt Extends Playground for Prompt Variables and API Integration
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@everyprompt extends Playground in a similar way: Putting variables in prompts and giving you a single button to go from prompt to API. Has nice developer-oriented touches in the UI too — e.g. displaying invisible chars as ghosts. https://
everyprompt.com