@svpino: How to become GOD-LEVEL with Large Language Models. Here are 50 hands-on projects with solutions that will teach you ho…

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A tweet promoting a book containing 50 hands-on projects covering LLM topics like tokenization, embeddings, and attention, using Python and PyTorch.

How to become GOD-LEVEL with Large Language Models. Here are 50 hands-on projects with solutions that will teach you how Large Language Models work. You don't need to solve all 50, but if you do, you'll be at the top 0.01% of the field. It's all Python + Pytorch + SciKit-Learn + Pandas + Numpy + Matplotlib + Seaborn. Here are the 50 problems from the book (link below): Tokenization 1. Three tokenization schemes 2. Book lengths in characters, words, and tokens 3. Pandas frequency tables of token lengths 4. Token lengths in characters and bytes 5. Is tokenization compression? 6. Tokenization and compression in different languages 7. Translating between tokenizers Embeddings 8. Distribution of cosine similarities 9. Sequential cosine similarity 10. Sequential number cosine similarity 11. Network graphs of cosine similarities 12. RSA to compare GPT-2 & BERT embeddings 13. Word similarity via distance and cosine 14. Linear semantic axes 15. Analogy vectors Output logits 16. Softmax probability distributions 17. Probabilistic token selection 18. Token prediction accuracy 19. LLM loss function 20. Perplexity over sequences, texts, and models 21. Predict token position with linear and logistic regressions 22. Evaluating models with HellaSwag 23. Measuring language biases Transformer outputs 24. Cosine similarities within and across layers 25. Category selectivity via cosine similarity 26. Current layer = previous layer + adjustments 27. Impact of layer-specific noise and scaling 28. Effective dimensionality of hidden layers 29. Hidden state dimensionality reduction 30. Sentiment analysis with decision trees 31. Logit lens 32. Patching hidden states in indirect object identification Attention 33. QKV weights characteristics 34. QKV activation characteristics 35. Raw and softmax attention scores 36. Characteristics of attention adjustment magnitudes 37. Token prediction and attention KL divergences 38. Laminar profile of RSA and category selectivity 39. Token frequency, attention adjustments, QK^T 40. Downstream impacts of head silencing 41. Patching heads in IOI MLP 42. MLP weights and activations characteristics 43. Characterizing the MLP progression 44. Grammar tuning in MLP projections 45. Minkowski distance, mutual information, and token positions 46. Statistics-based lesioning in MLP neurons 47. Supervised probing with XGBoost 48. "Can" vs. "can't" classification via logistic regression 49. Successive median-replacement of MLP activations 50. Recommender systems with MLP projections Book link below.
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Cached at: 07/14/26, 02:26 PM

How to become GOD-LEVEL with Large Language Models.

Here are 50 hands-on projects with solutions that will teach you how Large Language Models work.

You don’t need to solve all 50, but if you do, you’ll be at the top 0.01% of the field.

It’s all Python + Pytorch + SciKit-Learn + Pandas + Numpy + Matplotlib + Seaborn.

Here are the 50 problems from the book (link below):

Tokenization

  1. Three tokenization schemes
  2. Book lengths in characters, words, and tokens
  3. Pandas frequency tables of token lengths
  4. Token lengths in characters and bytes
  5. Is tokenization compression?
  6. Tokenization and compression in different languages
  7. Translating between tokenizers

Embeddings

  1. Distribution of cosine similarities
  2. Sequential cosine similarity
  3. Sequential number cosine similarity
  4. Network graphs of cosine similarities
  5. RSA to compare GPT-2 & BERT embeddings
  6. Word similarity via distance and cosine
  7. Linear semantic axes
  8. Analogy vectors

Output logits

  1. Softmax probability distributions
  2. Probabilistic token selection
  3. Token prediction accuracy
  4. LLM loss function
  5. Perplexity over sequences, texts, and models
  6. Predict token position with linear and logistic regressions
  7. Evaluating models with HellaSwag
  8. Measuring language biases

Transformer outputs

  1. Cosine similarities within and across layers
  2. Category selectivity via cosine similarity
  3. Current layer = previous layer + adjustments
  4. Impact of layer-specific noise and scaling
  5. Effective dimensionality of hidden layers
  6. Hidden state dimensionality reduction
  7. Sentiment analysis with decision trees
  8. Logit lens
  9. Patching hidden states in indirect object identification

Attention

  1. QKV weights characteristics
  2. QKV activation characteristics
  3. Raw and softmax attention scores
  4. Characteristics of attention adjustment magnitudes
  5. Token prediction and attention KL divergences
  6. Laminar profile of RSA and category selectivity
  7. Token frequency, attention adjustments, QK^T
  8. Downstream impacts of head silencing
  9. Patching heads in IOI

MLP

  1. MLP weights and activations characteristics
  2. Characterizing the MLP progression
  3. Grammar tuning in MLP projections
  4. Minkowski distance, mutual information, and token positions
  5. Statistics-based lesioning in MLP neurons
  6. Supervised probing with XGBoost
  7. “Can” vs. “can’t” classification via logistic regression
  8. Successive median-replacement of MLP activations
  9. Recommender systems with MLP projections

Book link below.

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