Llama from scratch, or how to implement a paper without crying

In this web content, the author shares their experience implementing a paper and provides tips for doing so. They discuss implementing a scaled-down version of Llama, a transformer-based model for language modeling, by breaking down the paper and implementing its components one by one. They emphasize the importance of testing and evaluating the model as it is being developed. The author also explains how to set up the dataset, create helper functions, and train the model. They highlight the need to understand the loss function and interpret the results to ensure the model is progressing effectively. The author then introduces specific modifications mentioned in the Llama paper, such as RMSNorm, rotary embeddings, and SwiGLU activation function, and suggests incorporating them into the model iteratively.


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