More Questions to Chat GPT on GPT and LLMs - Meta conversations

 Here are more questions on that example from the LLM book.

https://github.com/rasbt/LLMs-from-scratch/blob/main/ch05/01_main-chapter-code/gpt_train.py

 

From Chat Gpt - Open AI Response

Yes, AI and Large Language Models (LLMs) typically start with randomly initialized weights, though more sophisticated methods have evolved from this simple technique. A random starting point is a crucial part of the training process that allows the model to learn and differentiate between features. 

Why random initialization is necessary

Breaks symmetry: If all the weights in a network were initialized to the same value, every neuron in a layer would learn the exact same features. This prevents the network from learning anything useful. By starting with random, different weights, each neuron is forced to compute something different from its neighbors, allowing for a more diverse and robust learning process.

Enables learning from data: With random starting weights, the model's initial predictions are essentially gibberish. The training process then uses a feedback loop to refine these initial guesses. The model compares its predictions to the actual data, calculates an error, and uses an algorithm called backpropagation to adjust the weights, thereby reducing the error. 

Modern weight initialization methods

While basic random initialization is the foundation, researchers have developed more specific, calculated methods to optimize the training process and prevent common problems. 

Xavier (or Glorot) Initialization: This method is designed for networks using sigmoid or tanh activation functions. It sets the initial weights based on the number of input and output neurons in a layer to maintain a consistent variance of activations throughout the network.

He Initialization: Optimized for networks th

 

Great question! The key point is that your GPTModel does NOT have pre-trained weights from GPT-2 or any other large model. It starts with random weights.

 

 

Great question! The key point is that your GPTModel does NOT have pre-trained weights from GPT-2 or any other large model. It starts with random weights.

 

Why You Can't Just Start Chatting

Pre-trained GPT models (like OpenAI's GPT-2/3/4) have already learned language patterns, facts, and conversational skills from huge datasets.

Your GPTModel in this code is initialized with random weights. It knows nothing about language, grammar, or conversation at first.

Training on "The Verdict" teaches your model only the patterns and style from that story. Before training, it can't generate meaningful text.

What Training Does

Training adjusts the model's weights so it can predict and generate text similar to "The Verdict."

Without training, the model's outputs would be random and incoherent.

Summary

If you had a pre-trained GPT-2 model (with weights from OpenAI), you could start chatting immediately.

With this code, you must train the model first, because it starts from scratch and knows nothing until it learns from your data.

In short:

You need to train because your model is not pre-trained. Training gives it the ability to gene

 

 

After training, the model’s weights are stored in a dictionary-like structure (state_dict), which maps layer names to tensors.

 

Snippet: Saving and Loading Weights

 

These vectors represent the meaning of each token in a high-dimensional space.

 

Processing

The sequence of embedding vectors is passed through the transformer layers, which use the learned weights to generate the next token or predict text.

 

What Does It "Look Like" in the Model?

The model does not store the phrase "Every effort moves you" directly.

It stores embedding vectors for each token (e.g., for "Every", "effort", "moves", "you").

When you input the phrase, the model looks up the vectors for those tokens and processes them according to its learned weights.

 

Output might be:

 

Summary Table

Text    Token IDs   Embedding Vector (shape)

"Every effort moves you"    [8251, 1126, 1512, 345] [4, 768]

 

 

In short:

The model stores vectors for each token, not the phrase itself. When you input "Every effort moves you", it looks up those vectors and uses its learned weights to generate a continuation.

 

 

Great observation! The phrase "Every effort moves you" does not appear in "The Verdict" training text. Here’s how the model connects to it:

 

How the Model Handles Unseen Phrases

Tokenization

The GPT-2 tokenizer splits "Every effort moves you" into token IDs, using its pre-existing vocabulary.

Example:

 

Embeddings

The model has an embedding vector for each token in the vocabulary, including "Every", "effort", "moves", "you".

 

If a token appeared in "The Verdict", its embedding was updated during training.

If a token did not appear, its embedding remains mostly random.

Generation

When you use "Every effort moves you" as a prompt:

 

The model looks up the embeddings for those tokens.

It tries to generate a continuation based on what it learned from "The Verdict".

Since the phrase is new, the model will use its knowledge of similar patterns, grammar, and style from "The Verdict" to continue the prompt.

What Does This Mean for Output?

The model does not "know" the phrase directly.

It can process the phrase because it has embeddings for each token.

The quality of the continuation depends on how well the model learned general patterns from "The Verdict".

If the tokens in the prompt were never seen in training, the output may be less coherent.

Summary Table

Phrase/Token    In Training Data?   Embedding Updated?  Model "Understands"?

"Every effort moves you"    No  No  Not really

"you"   Yes Yes Yes

"effort"    Maybe   Maybe   Maybe

In short:

The model can process any phrase using its vocabulary, but it only "understands" and generates meaningful continuations for patterns and words it saw during training. For unseen phrases like "Every effort moves you", it will try to continue in the style of "The Verdict", but the connection is indirect and based on learned general language patterns

 

 

"Every effort moves you" is a prompt you provide to the model after training.

This phrase does not appear in "The Verdict" text, nor is it referenced by the story.

The model, after training, has learned the style, vocabulary, and sentence structure from "The Verdict"—including how words like "every" and "beauty" are used in context.

What happens when you use the prompt:

 

The model will try to continue "Every effort moves you" in the style and language patterns it learned from "The Verdict".

If the word "every" appears in the training text (as in "every form of beauty"), the model has learned how it is used in context, but it has not memorized your prompt.

The output will be a new sentence or paragraph that mimics the literary style, possibly referencing similar themes (effort, beauty, movement) if those concepts were present in the training data.

Summary:

The connection is stylistic and linguistic, not semantic or direct. The model uses what it learned from "The Verdict" to generate text after your prompt, but it does not "know" or "connect" your prompt to any specific line in the story. It simply continues in the learned style.