ReduceDocumentsChain But what if you have too many documents to fit into a single a prompt? That's where ReduceDocumentsChain comes into play It recursively combines documents together Docs: https://
api.python.langchain.com/en/latest/chai
ns/langchain.chains.combine_documents.reduce.ReduceDocumentsChain.html#langchain.chains.combine_documents.reduce.ReduceDocumentsChain
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ReduceDocumentsChain: Combining Multiple Documents in LLM Prompts
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Combining Documents with LLMs: LangChain Documentation Improvements
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Documents x LLMs Combining documents with LLMs is a key part of retrieval and chaining We've improved our @langchain reference documentation across the 5 major CombineDocumentsChains and helper functions to help with clarity and understanding of how these work
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Control Document Metadata in LLM Prompts with format_document
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`format_document` Want to control which metadata keys show up in the prompt? This helper function is rarely exposed, but is key to combining documents with LLMs It takes a Document and formats it into a string using a PromptTemplate Docs: https://
api.python.langchain.com/en/latest/sche
ma/langchain.schema.prompt_template.format_document.html#langchain.schema.prompt_template.format_document
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OpenCV University Independence Day Sale 25% Off All Courses
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Enroll Now for a blazing 25% Off on all our Courses with OpenCV University's Independence Day Sale!!https://t.co/p5t0Ha7DG0#ai #opencvuniversity #computervision pic.twitter.com/wOBMbLP9kr
— Satya Mallick (@LearnOpenCV) 5 juillet 2023Enroll Now for a blazing 25% Off on all our Courses with OpenCV University's Independence Day Sale!! https://
opencv.org/university/ #ai #opencvuniversity #computervision -
Appreciation for MPT Article and FSDP Implementation Question
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Thanks for writing & sharing your latest article on MPT. Really well written and has just the right level of detail .
One question though. You wrote > "The entire training framework is based upon PyTorch’s Fully Sharded Data Parallel (FSDP) package and uses no pipeline or -
Hugging Face Hub API Documentation Guide
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your best bet is https://
huggingface.co/docs/hub/api but not super exhaustive (yet) -
Better QA Over Code Through Splitting Technique
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Awesome stuff from @cristobal_dev – better qa over code, thanks to splitting
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Introduction to Autoencoders in Deep Learning
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Introduction to autoencoders. https://
bit.ly/3oco9su #AI #MachineLearning #DeepLearning #LLMs #DataScience -
Beyond Homogeneous Transformers: Loss Functions and Inference Optimization
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i think your position comes from a fundamentally incomplete estimation of the parts of the graph that are not a homogeneous transformer — the loss function, the beam search, things like activation checkpointing that isnt simply autodiff, token fusion style tricks. caffe style
