While the proposed approach is promising, there are several limitations and potential areas for future work:
The most notable examples of LLMs include BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly Optimized BERT Pretraining Approach), and XLNet (Extreme Language Modeling). These models have achieved state-of-the-art results in various NLP tasks, such as language translation, sentiment analysis, and question-answering. Build A Large Language Model -from Scratch- Pdf -2021
# Initialize the model, optimizer, and loss function model = LargeLanguageModel(vocab_size, hidden_size, num_layers) optimizer = optim.Adam(model.parameters(), lr=1e-4) criterion = nn.CrossEntropyLoss() While the proposed approach is promising, there are
: The structural unit that stacks multiple attention and feed-forward layers to process complex linguistic patterns. The Step-by-Step Build Process Build an LLM from Scratch 3: Coding attention mechanisms While the proposed approach is promising
I hope this helps! Let me know if you have any further questions.