Let's reproduce GPT-2 (124M)

Tutorial: Reproducing GPT-2 (124M) by Andrej Karpathy

In this tutorial, we will walk through the process of reproducing the GPT-2 (124M) model from scratch as demonstrated by Andrej Karpathy in his YouTube video. The video covers building the GPT-2 network, optimizing training for speed, setting up the training run with specific hyperparameters, and generating model results.

Steps to Reproduce GPT-2 (124M):

1. Exploring GPT-2 Checkpoint:

   • Start by exploring the GPT-2 (124M) OpenAI checkpoint mentioned in the video.

2. Implementing GPT-2 nn.Module:

   • Implement the GPT-2 neural network module as detailed in the video.
   • Load the huggingface/GPT-2 parameters and implement the forward pass to get logits.

3. Training the Model:

   • Prepare data batches and compute logits.
   • Implement cross-entropy loss and optimization loop to train the model.

4. Optimizing for Speed:

   • Explore techniques to make training faster, such as using GPUs, mixed precision, and Tensor Cores.
   • Experiment with different precision modes like float16 and bfloat16 for improved speed.

5. Setting Hyperparameters:

   • Define hyperparameters like learning rate scheduler, batch size schedule, and weight decay.
   • Implement gradient clipping and gradient accumulation for better training performance.

6. Validation and Evaluation:

   • Split data for validation, calculate validation loss, and perform model evaluation.
   • Start the training run and analyze results the next morning.

7. Additional Resources:

   • Check out the supplementary links provided in the video for further reading on related papers and repositories.
   • Explore the GitHub repositories shared by Andrej Karpathy for detailed code implementations.

Conclusion:

Reproducing the GPT-2 (124M) model from scratch involves building the network, optimizing training for speed, setting hyperparameters, and evaluating the results. By following the steps outlined in the video and utilizing the provided resources, you can learn to create and train your own GPT-2 model.

For any corrections or updates, refer to the build-nanogpt GitHub repo shared by Andrej Karpathy. Happy experimenting with GPT-2 replication!