TOOLS

LLM Configuration Serialization in JSON and YAML Formats

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14 December 2022 17h54

Serialization of LLM Configurations Have a favorite temperature/parameter setting for your LLM that you want store/version not in code? Now you can! @AkashSamant4 made it super easy to read/write LLM configurations to/from disk in json and yaml format

LangChain Documentation Updates: New Functionality and Serialization

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14 December 2022 17h54

Check out these docs for a walkthrough of this new functionality: https://
langchain.readthedocs.io/en/latest/exam
ples/prompts/llm_functionality.html
… Check out these docs for an overview of serialization: https://
langchain.readthedocs.io/en/latest/exam
ples/prompts/llm_serialization.html
… And of course, check out the GitHub if you want to flag an issue or open a PR! https://
github.com/hwchase17/lang
chain
…

New abstraction enables returning generation metadata like logprobs

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14 December 2022 17h54

This abstraction also opens up the door to returning more information about each generation For example, the logprobs associated with a generation (something I know @alexgraveley would love)

Map-Reduce Chain Speed Optimization Through LLM Call Batching

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14 December 2022 17h54

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

LangChain Introduces LLM Input Batch Processing

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14 December 2022 17h54

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

→ View original post on X — @benthompson

App Store Distribution Channel for AI Applications

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@benthompson

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14 December 2022 17h40

The stores could be in the App Store

→ View original post on X — @abacusai

30 MLOps Requirements: Abacus AI Simplifies Machine Learning Systems

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@abacusai

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14 December 2022 17h00

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")