![]() ![]() It has been trained on a dataset of human conversations, which allows it to generate more natural and engaging responses. Chat optimization: Llama 2-Chat is a specialized version of Llama 2 that is optimized for engaging in two-way conversations.Whereas, the maximum size of Llama is 65 billion parameters. Model size: Llama 2 is available in three sizes: 7 billion parameters, 13 billion parameters, and 70 billion parameters.This gives it a larger knowledge base and allows it to generate more accurate responses. Training data: Llama 2 is trained on 40% more tokens than Llama, a total of 2 trillion tokens.Here are some of the key differences between Llama 2 and Llama: See one of them here Llama 2 What's New in Llama 2 You can search it on Hugging Face website. In the model path, you need to have weights for Llama in GGML format and then store them into the models folder. Output = llm("Q: Name the planets in the solar system? A: ", max_tokens=128, stop=, echo=True) Llm = Llama(model_path="./models/7B/ggml-model.bin") It uses the transformer architecture and was trained on 1.4 trillion tokens extracted by web scraping Wikipedia, GitHub, Stack Exchange, Books from Project Gutenberg, scientific papers on ArXiv. They claimed the 13 billion parameter model outperformed 175 billion parameters of GPT-3 model. ![]() Meta AI researchers focused on scaling the model's performance by increasing the volume of training data, rather than the number of parameters. It includes a range of model sizes from 7 billion to 65 billion parameters. Llama stands for Large Language Model Meta AI. ![]()
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |