This is a total game-changer for anyone buried in research! I’ve been through the struggle of juggling endless papers and trying to piece together a coherent narrative for hundreds of videos and now our courses—and this tool changes everything. @answerthisio is a single platform
@whats_ai
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Building Email Agents with Hugging Face smolagents Library
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Super excited to share our post with @xaiguydotagi (and possibly a follow-up?!). In this article, we show how to build an email agent using the recently released smolagents library by @huggingface ! And so will you, with a complete tutorial and Google Colab to follow along!
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Python Course: Google Colab and Jupyter Notebooks Guide
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Good morning! Here's the third video lesson of our Python course: our introduction to Google Colab and Jupyter Notebooks. And, more importantly, some useful tips on when to use which. These are two fantastic environments that let you write code, see the outputs right away, add
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Jupyter vs Colab: Essential Differences for Beginners
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New video!! A shift from the usual deep dives. Going back to basics: The BIGGEST Differences Between Jupyter and Colab You Need to Know! It is a (very useful) introduction to Colab and Jupyter for beginners that we used in our new Python course, which is now live on Towards AI
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Quantization Tutorial: Reduce AI Model Operational Costs
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Bonus: We give a full tutorial on Quantization, including how it works, the different techniques, and more. Bonus 2: We have a detailed Google Colab for you in there! Want to dive deeper? Read our blueprint to reduce AI model operational costs with quantization:
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4-bit quantization reduces inference overhead for resource-constrained deployment
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While the 4‑bit model experiences a 30–45% increase in inference time due to dequantization overhead, the output remains coherent and accurate. This approach makes deploying large models on resource-constrained hardware much more feasible.
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Llama 3 8B quantized to 4-bit reduces memory usage by 64%
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We converted a full-precision (FP16) Llama 3 8B model into a 4‑bit version using Hugging Face’s bitsandbytes library with the “nf4” configuration. This reduced the model’s memory usage from ~15 GB to ~5.4 GB—a savings of roughly 64%.
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Model Quantization Reduces AI Operational Costs Without Quality Loss
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Some insights from our recent post with @DamiBenveniste on his (amazing) newsletter: The AiEdge Newsletter! In this post, we shared how model quantization can dramatically cut AI model operational costs by reducing memory footprints **without sacrificing output quality** with
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Nvidia GTC Event Innovations in AI Technology Showcase
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Good morning, everyone! In this iteration, I'm sharing something I got from the Nvidia GTC event I attended last week. GTC is Nvidia’s annual event, and I had the chance to check out some incredible new technology being shared in dozens of amazing talks. One initiative that
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Open-Sora 2.0 Achieves Sora-Level Video AI for $200K
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New video! How Open-Sora 2.0 Built Sora-Level AI for $200K (Full Breakdown) https://
youtu.be/gMdvyGVICfA