Bet the 100Kth model was a Whisper model
LLMS
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UC Berkeley and Peking U Propose Unsupervised Contrast-Consistent Search
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Finding Truth in LLMs: UC Berkeley & Peking U Propose Unsupervised Contrast-Consistent Search https://
syncedreview.com/2022/12/14/fin
ding-truth-in-llms-uc-berkeley-peking-u-propose-unsupervised-contrast-consistent-search/
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Fine-tuned Models Testing on Public Datasets
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Interesting. What kind of data are you inferring on? Is there some publicly available data that you’re testing on? Would love to test out some of our fine-tuned models against it.
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Prompt Engineering: Better Results Working Directly With Models
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No longer using ChatGPT super often. As a diehard prompt engineer, I can nearly always get better results by working with the models directly. BUT For the average person, chat is the best and easiest way to get language models to do what you want. Conversation is prompting.
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LLM Configuration Serialization in JSON and YAML Formats
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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
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LangChain Documentation Updates: New Functionality and Serialization
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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
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LLM Interface Enhancement: Extended Output with Top Results and Token Counts
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Returning extra information Previously, the LLM only returned the most likely string Now there is a new interface to return more information: the top n strings for each input, as well as LLM specific information (h/t @0xAwill for adding the token counts you see below)
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New abstraction enables returning generation metadata like logprobs
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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)
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Map-Reduce Chain Speed Optimization Through LLM Call Batching
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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
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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