Yes, you are right. I think CLIP used to be the standard but now SigLIP seems to be a more popular choice
@rasbt
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MacBook RAM recommendations for local AI development work
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Glad you found my book helpful, and congrats on landing a job in AI!
Regarding the Mac… I am actually on a MacBook Air for (and have a 32 Gb Pro for work) as I run AI experiments on cloud machines anyways. But yeah, if you plan to run most stuff locally, I would max out the RAM -
Catching Up on LLM Developments and Book Progress
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Ha, yes. And there's always more to catch up on in LLM land. PS: I hope you are liking the book so far!
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Understanding Multimodal LLMs: Decoder-Only and Cross-Attention Approaches
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If you are curious how Multimodal LLMs work, I wrote a new article to explain the two main approaches, decoder-only- and cross-attention-style: https://
magazine.sebastianraschka.com/p/understandin
g-multimodal-llms
…
Plus, I reviewed and summarized the 10 latest research papers to see how it's done in practice.
Happy reading! -
Bonus Material Added: GPT to Llama 3.2 Model Conversion
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Thanks for the kind words, and I am glad you got lots out of it! Btw in case you feel bored, I added a bunch of bonus material to the GitHub repo recently. For example the conversion to a Llama 3.2 model: https://
github.com/rasbt/LLMs-fro
m-scratch/tree/main/ch05/07_gpt_to_llama
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Upcoming Blog Article on Multimodal LLMs Coming Tomorrow
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Ha, what a timely question. Spoiler: I meant to post an extensive blog article on multimodal LLMs today but got a bit behind with editing and uploading. But keep an eye out for it tomorrow (
https://
magazine.sebastianraschka.com) -
CPU and GPU Compatibility: Code Automatically Adapts to Available Hardware
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Interesting question. Not sure this is possible. However, this also shouldn't be necessary for the book. The code works on both CPU and GPU machines (the code is written that it automatically uses a compatible GPU if available)
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BPE Implementations Comparison and Julia Experiments Updates
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Awesome, glad to hear! And I am curious about the Julia experiments, but absolutely no rush 🙂
Btw I also did some quick comparisons with some other BPE implementations here: https://
github.com/rasbt/LLMs-fro
m-scratch/blob/main/ch02/02_bonus_bytepair-encoder/compare-bpe-tiktoken.ipynb
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(I also have my own custom one that I plan to polish up and add some time…) -
Python and PyTorch prerequisites for machine learning course
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Thanks for your interest! I'd say familiarity with Python programming would be required. Ideally some light machine learning or deep learning background with PyTorch. However, I do have a ~40-page introduction to the essentials (Appendix A), which could get you up to speed quick
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MacBook Air M3 with 24GB RAM Handles Llama 3.2 Well
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Yeah. But in the meantime, the MacBook Air M3 24 Gb is just fine for Llama 3.2 😛