We need more kudos for researchers who are publishing openly usually outside of the spotlight! https://
huggingface.co/ai21labs/Jamba
-v0.1/discussions/1
…
RESEARCH
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Recognizing Open Research: Kudos for Overlooked AI Researchers
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LLMs and Doctors: Collaboration Over Replacement in Medicine
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“It’s no longer a question of whether LLMs will replace doctors in the clinic — they won’t — but how humans and machines will work together to make medicine better for everyone,” says @Stanford CERC researcher Ethan Goh.
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New Research Paper from Boknilev Group on AI
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this is the paper, it's quite new, from @boknilev
's group! https://
arxiv.org/pdf/2403.05846
.pdf
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Diffusion Lens reveals abstract-to-concrete representation layers in image encoders
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Diffusion Lens is a pretty neat new paper, you can see a text-to-image encoder's representation of a giraffe getting less and less abstract with every layer
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Full Fine-Tuning vs Other Methods: Overfitting Prevention
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Not necessarily. Done right, full fine-tuning is usually better… my assumption for why this prevents overfitting is it’s not really affecting the parameters as much as a FT. Total speculation, I may be wrong.
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LoRA QLoRA Fine-tuning Prevents Overfitting Better
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While LoRA/QLoRA isn’t as powerful as full fine-tuning, it often can prevent overfitting from suboptimal tuning
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Microsoft Expands AI Model Availability on Hugging Face Platform
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They do a bit already: https://
huggingface.co/microsoft but let's for more! -
Mistral 7B Extended Context Training and Dataset Improvements
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On the model side, longer context length for the 7B! If you could train over the new Mistral 7B w/ 32K that’d be awesome. As for the dataset, I haven’t found many issues with it 🙂
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Cancer Systems Disease Review Integrates AI Analysis Perspectives
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A comprehensive 5★ perspective/review on cancer as a complex, systemic disease with all the hallmarks https://
cell.com/cell/fulltext/
S0092-8674(24)00175-2
… @CellCellPress by @CharlesSwanton and colleagues -
Team Commitment to Open Source Through Hugging Face Releases
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A bit biased of course but for me, the best indicator of how much a team cares about community and open-source is the level of effort they put into their Hugging Face model/dataset/space/paper releases! It means they're thinking a lot about how to make it easier for all to