Top Tweets for #30daysofllms
day 21 of #30DaysOfLLMs
continued my Transformer deep dive
- realized tinyGPT uses a decoder-only architecture (modern GPT-style), not the original encoder-decoder design
focusin on the original arch first, will cross-ref what's changed later when I have clarity
#30daysOfCode

day 20 of #30DaysOfLLMs
continued my deep dive on Transformers
- tried to read & dissect 'Attention Is All You Need' - the 2017 paper that introduced the Transformer architecture
fell asleep halfway, so much technical jargon lol
try again tomorrow
#30daysOfCode

Day 19 of #30DaysOfLLMs
circled back to Transformers - the beating heart of modern LLMs
- learned its origins: original transformer architecture introduced by google in 2017
- had encoder&decoder layers tho modern LLMs use diff approaches (eg GPT = decoder-only)
#30daysOfCode

day 18 of #30DaysOfLLMs
still on tokenization
- learnd about the BPE (Byte Pair Encoding) algo and why it's commonly used for subword tokenization
- attempted a BPE subword tokenizer frm scratch, trained on my dataset
got my hands dirty, learned a few things...
#30DaysOfCode

day 17 of #30DaysOfLLMs
went down the rabbit hole of tokenization
- explored tokenization libraries (hugging face, SentencePiece) and how to integrate them with tinyGPT
- learned the difference btwn trainin a tokenizer on ur own dataset vs usin a pretrained one
#30DaysOfCode

day 16 of #30DaysOfLLMs
more text generation(inference) tests w tinyGPT
- learned to scale logits with temperature before samplin - controls how risky vs safe next token predictions are
1 = neutral, below 1 = predictable, above 1 = riskier/random
fun experiment
#30DaysOfCode

I think #100DaysOfCode is kind of overrated should I do #30DaysOfLLMs instead - sharing cool, informative blogs that even people with minimal ML background can understand
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