This now means that certain chains see a MASSIVE speed up. For example, the map-reduce chain can batch the map calls to the LLM, resulting in a ~75% drop in time
AI
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LangChain Introduces LLM Input Batch Processing
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Batching of inputs Previously, the only interface for calling an LLM was to pass in a single string. With big motivation from @thepromptking, there is now an interface that takes in and returns results for multiple inputs
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Calculating Token Count for LLM Context Windows
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Estimating the number of tokens Each model has a context window with a certain length, where you can only pass in strings of up to a certain length There now exists a method on each LLM class to calculate the number of tokens for a string in that model
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GPT2 Tokenizer as Default Method with Overridable Class
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The default method for calculating this uses @huggingface
's GPT2 tokenizer But since its a class method, if different models use different tokenizers this can be overwritten h/t @brucehammer for implementing the logic, and then Zac on Discord for suggesting this interface -
LangChain LLM Interface Improvements and New Features
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There's been some additions to the LLM interface over the past few days: Method to estimate # of tokens, batching of inputs, returning extra information, serialization of LLM configurations @AkashSamant4 @thepromptking @0xAwill Let's walk through these improvements:
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AI Princess NFT: Web3 Digital Collectible Launch
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.eth Born to rule the Web3 World! https://
opensea.io/assets/ethereu
m/0x57f1887a8bf19b14fc0df6fd9b2acc9af147ea85/42390964498321167479707823290384787268327564629588650698736232606645700561362
… #AIPrincess #MontrealAI #WeAreRoyalty -
Should Local Police Departments Deploy Lethal Robots?
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Should Local Police Departments Deploy Lethal Robots? #RuleoftheRobots
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From Boxing Ring to JPMorgan: Data Science Leadership Journey
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From the ring to The Street, Tiffany Perkins-Munn has always employed data to guide her business success. Learn about her journey from TITLE Boxing Club to @JPmorgan in The Finance and Insurance Data Science Innovator's Playbook. https://
domino.buzz/3UH50sH #MLOps -
30 MLOps Requirements: Abacus AI Simplifies Machine Learning Systems
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Building Machine Learning Systems is hard. Here are 30 requirements for an MLOps environment. At @abacusai we worry about this for you. You bring the data, we do the rest. (Source: "Requirements and Reference Architecture for MLOps: Insights from Industry")