There may also be more usecases for custom LLMs atm: – assistants for all sorts of drafting (don’t want to post sensitive materials into eg ChatGPT)
– Q&A for legal, medical, or financial data & docs
– customer chatbots that have some domain knowledge wrt to the company products
@rasbt
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Custom LLMs Use Cases: Privacy-Sensitive Drafting and Domain Knowledge
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Rethinking Academic Requirements Beyond Paper Publications
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No papers no PhD. But hey, maybe we can go back to the earlier days when a solid thesis (in academia or industry) was enough, and students weren’t pressured to get >= 3 top-tier conference papers accepted before graduation.
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Need Multimodal ChatGPT and Bard for Screenshot Analysis
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We need multimodal ChatGPT or Bard so we can copy-paste the screenshot and ask
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Building Things: The Key to Learning Deep Learning Fundamentals
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The secret sauce for getting into modern deep learning & AI. For me "building things" has been a powerful motivator with respect to learning how things work under the hood (later).
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NN+Gzip Follow-up: Compression Algorithms Comparison Study
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If you liked my NN+Gzip article last week (
https://
magazine.sebastianraschka.com/p/large-langua
ge-models-and-nearest
…), you'll probably love @abhi9u & @alepiad 's follow-up. This one looks at the NN+Gzip method from a compression algo perspective comparing Gzip, Huffman coding, LZ77/LZ4, and Bzip2: https://
codeconfessions.substack.com/p/lz77-is-all-
you-need/
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Model Performance Comparison with 11B T5 Parameters
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Probably about the same as the 11B T5 model
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Installation and requirements setup for deep learning workshop
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Maybe this installation + requirements.txt from my recent DL workshop is useful here: https://
github.com/rasbt/scipy202
3-deeplearning/tree/main/00-2_python-libraries-for-workshop
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Power User PyTorch Nightly and Lightning GitHub Installation
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As a "power user" I usually install the PyTorch nightly and the lightning from the GitHub branch .
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Fine-tuning smaller models for specialized tasks with custom data
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Yes, it's more impressive. But if you are a small company that specializes on a specific task and has some custom data, I'd say that finetuning a smaller (e.g. 1.5 B) model is the way to go.
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Reference Papers for Llama 2 and DeBERTa Models
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3/3
Here are the papers for reference: Llama 2: Open Foundation and Fine-Tuned Chat Models: https://
arxiv.org/abs/2307.09288 DeBERTa: Decoding-enhanced BERT with Disentangled Attention: https://
arxiv.org/abs/2006.03654