Best of GPT — February 2025

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   Article

   Data Engineer Things·1y

   10 minutes are all you need to understand how Transformers work in LLM

   Understanding how transformers work in large language models (LLMs) can be achieved quickly by breaking down the steps involved in the process. Starting from tokenization, where input data is converted into tokens, these tokens are then embedded into numerical representations understood by the model. These embeddings are processed through multiple transformer layers that use attention mechanisms to determine the importance of each token in relation to others. Finally, the processed data is projected back onto the vocabulary to predict the next token in a sequence. This foundational knowledge helps in exploring further intricacies of models like GPT-2.

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   Video

   Fireship·1y

   GPT-4.5 shocks the world with its lack of intelligence...

   GPT-4.5 was released by OpenAI, but it has disappointed many due to its high cost and lack of novel capabilities. The launch focused on its ability to chat in a more natural way and its lower hallucination rate, but many found it underwhelming. The model is extremely expensive, costing $150 per million output tokens and accessible only to $200 per month Pro users. Despite scaling up the number of parameters and compute, the improvements seem marginal. The AI model is particularly criticized for its performance in programming tasks compared to other existing models.

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   Video

   Hacker News·1y

   Deep Dive into LLMs like ChatGPT

   Large language models (LLMs) such as ChatGPT are built through a complex pre-training process involving the downloading and processing of large quantities of diverse, high-quality internet texts. Common Crawl data, along with filtering steps like URL filtering, text extraction, and language filtering, are critical components. Tokenization converts these texts into a sequence of symbols for neural networks to process. These networks are trained to model the statistical relationships between tokens to predict the next token in a sequence. Inference is generating new data from the trained model by predicting subsequent tokens based on a given input.

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