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Pre-training GPT-2 (124M)on Hindi(Devanagari) Text from scratch: A Journey Through Tokenization…
- An attempt was made to pre-train the GPT-2 124M model on Hindi Devanagari text, using the Hin_Deva dataset sourced from books, articles, and websites.
- Challenges arose during tokenization due to the GPT-2 tokenizer's optimization for English, which fragmented Hindi text into suboptimal chunks.
- Despite computational resource limitations, the model was pre-trained on a cluster equipped with 8× NVIDIA A100 SXM4–80GB GPUs from Lambda Labs.
- The model was trained for 19,073 steps on a batch size of 524,288 tokens, costing approximately $82 in total.
- Results showed lower loss values compared to OpenAI's model, indicating improvements in token prediction for Hindi.
- Generated sample sentences in Hindi were somewhat coherent, suggesting the model learned useful representations despite tokenization challenges.
- Following the Hindi pre-training, a subsequent pre-training run was conducted on an English dataset from the Fineweb dataset.
- The English pre-training involved running the model for 38,146 steps, costing around $116 in total, and using the same batch size.
- Model performance was evaluated using the Hellaswag benchmark, showcasing the effectiveness of traditional methods in conventional settings.
- Future work may involve exploring custom tokenizers for non-Latin languages and further optimizing the training pipeline for enhanced performance.
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