What are the fundamental principles behind a Large Language Model? For example, how does the Transformer architecture come into play, and what purpose does the attention mechanism serve? Is it practical to run these models locally for small to medium-scale projects, or should cloud solutions always be preferred? What preprocessing and fine-tuning approaches stand out for these models?
How does the Llama architecture work?
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Llama architecture is fundamentally based on the Transformer model, particularly excelling at capturing connections within text thanks to its attention mechanism. For small projects, you can try running it locally, but you shouldn’t push your RAM and GPU too hard—when I tested the MFM model locally, it pushed my 16GB RAM to its limits. The results were truly impressive after preprocessing with tokenization and fine-tuning on custom data, especially with Turkish documents.