MiniTorch https://
bit.ly/4eIe7DV
#AI #MachineLearning #DeepLearning #LLMs #DataScience
TOOLS
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MiniTorch: Open Source Deep Learning Framework for AI
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Version 18.2 Beta Disappoints Users with Underwhelming Performance
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I run the 18.2 beta and it’s quite underwhelming
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Add Custom Quick Actions Beyond Built-in AI Features
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This a very cool add – don't limit yourself to the built in quick actions (change length, change reading level, add emojis) but add your own quick actions! https://t.co/VFHYDzYcdt
— Harrison Chase (@hwchase17) 25 octobre 2024This a very cool add – don't limit yourself to the built in quick actions (change length, change reading level, add emojis) but add your own quick actions!
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Hugging Face: Follow Your Favorite Open-Source AI Organizations
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What's you favorite open-source AI organization? You can now follow them on @huggingface to get notified each time they release a new model, dataset, paper or app! https://t.co/3slR4NXFkS pic.twitter.com/oXsGN9Ol5B
— clem 🤗 (@ClementDelangue) 25 octobre 2024What's you favorite open-source AI organization? You can now follow them on @huggingface to get notified each time they release a new model, dataset, paper or app! https://
huggingface.co/organizations -
XetHub Integration Coming to Hugging Face Platform
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in a couple of weeks i would use XetHub (
@xetdata
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User frustration over GPT Builder access restrictions
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BRB getting tissues to soak up my tears (no access to GPT builder??)
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Combining Bi-Encoders and Cross-Encoders in RAG Systems
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Combining Both in RAG Systems To get the best of both worlds: Retrieve: Use Bi-Encoders as embedding-model to efficiently retrieve top candidate documents. Rerank: Use Cross-Encoders to rerank these candidates for better accuracy. Check this out 7/n
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Cross-Encoders: High Accuracy but Limited Scalability
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Cross-Encoders Cross-Encoders take both the query and document together as input and output a similarity score between 0 and 1. Although cross-encoders produce highly accurate similarity scores, they are not scalable (explained in next tweet). 5/n
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Bi-Encoders in RAG: Efficiency and Scalability Benefits
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Bi-Encoders in RAG Bi-encoders are a great choice for embedding models! Why • Efficiency: Pre-compute embeddings for all documents and store them in a vector store.
• Scalability: Then use fast ANN to search over millions of vectors 4/n -
Groq Infrastructure Supercharges GenAI Applications with Speed
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In this must-watch talk from World Summit AI 2024, @lifebypixels
, VP of Brand at Groq, dives deep into how the right infrastructure can supercharge your GenAI applications. See live demos showcasing the incredible speed and efficiency of Groq technology. https://
hubs.la/Q02VzwxQ0