Meta's Llama 3 with 8 billion to 70 billion parameters is designed to rival much larger models, focusing on multilingual and multimodal features from smaller domain-specific models.
The efficiency and performance boost of Meta's Llama 3 are attributed to a tokenizer with 128,000 tokens, better datasets, and additional fine-tuning steps after training.
Llama 3 was pre-trained on over 15 trillion tokens, showcasing seven times larger dataset and four times more code than its predecessor, emphasizing data quality control.
#llama-3 #large-language-model #tokenizer-efficiency #data-quality-control #multilingual-and-multimodal-models
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