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Llama was trained on only publicly available information, and was trained at various model sizes, with the intention to make it more accessible to different hardware. The model was exclusively a foundation model, [6] although the paper contained examples of instruction fine-tuned versions of the model. [2]
For example, training of the GPT-2 (i.e. a 1.5-billion-parameters model) in 2019 cost $50,000, while training of the PaLM (i.e. a 540-billion-parameters model) in 2022 cost $8 million, and Megatron-Turing NLG 530B (in 2021) cost around $11 million. [56] For Transformer-based LLM, training cost is much higher than inference cost.
1.5 trillion tokens Apache 2.0 Trained on crowdsourced open data Jurassic-2 [69] March 2023: AI21 Labs: Unknown Unknown Proprietary Multilingual [70] PaLM 2 (Pathways Language Model 2) May 2023: Google: 340 [71] 3.6 trillion tokens [71] 85,000 [57] Proprietary Was used in Bard chatbot. [72] Llama 2: July 2023: Meta AI: 70 [73] 2 trillion tokens ...
The Llama in question wasn’t an animal: Llama 2 was the follow-up release of Meta’s generative AI model—a would-be challenger to OpenAI’s GPT-4. The first Llama had come out a few months ...
On the MATH benchmark of competition level math word problems, for example, Meta's model posted a score of 73.8, compared to GPT-4o's 76.6 and Claude 3.5 Sonnet's 71.1. The model scored 88.6 on ...
llama.cpp is an open source software library that performs inference on various large language models such as Llama. [3] It is co-developed alongside the GGML project, a general-purpose tensor library. [4] Command-line tools are included with the library, [5] alongside a server with a simple web interface. [6] [7]
The new version of the model, called Llama 2, will be distributed by Microsoft through its Azure cloud service and will run on the Windows operating system, Meta said in a blog post, referring to ...
X-bar theory graph of the sentence "He studies linguistics at the university." Constituency is a one-to-one-or-more relation; every word in the sentence corresponds to one or more nodes in the tree diagram. Dependency, in contrast, is a one-to-one relation; every word in the sentence corresponds to exactly one node in the tree diagram.