![]() ![]() One may note, though, that scraping of data has become a thorny issue and the reason for many class-action suits too. ![]() Meta claims that these models "have demonstrated their competitiveness with existing open-source chat models, as well as competency that is equivalent to some proprietary models on evaluation sets we examined" but acknowledges that they still lag other models like OpenAI's GPT-4. Meta has also released Llama 2-Chat, a fine-tuned version of Llama 2 that is optimized for dialogue with the same parameter ranges. The newly-released Llama 2, according to Meta, is a collection of pre-trained and fine-tuned LLMs, ranging from 7 billion to 70 billion parameters. ![]() Meta says it chose a text from the 20 languages with the most speakers, focusing on those with Latin and Cyrillic alphabets, to train LLaMa. Like other LLMs, LLaMA takes a sequence of words as input and predicts the next word to generate text recursively. Its smallest model, LLaMA 7B, is trained on one trillion tokens. Meta says it has trained LLaMA 65B and LLaMA 33B on 1.4 trillion tokens. However, according to Meta, smaller models trained on more tokens -pieces of words - are easier to re-train and fine-tune for specific potential product use cases. ![]()
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