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Llama (Large Language Model Meta AI, formerly stylized as LLaMA) is a family of large language models (LLMs) released by Meta AI starting in February 2023. [2] [3] The latest version is Llama 3.3, released in December 2024. [4] Llama models are trained at different parameter sizes, ranging between 1B and 405B. [5]
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]
7.3 [77] Unknown Apache 2.0 Claude 2.1: November 2023: Anthropic Unknown Unknown Unknown: Proprietary Used in Claude chatbot. Has a context window of 200,000 tokens, or ~500 pages. [78] Grok-1 [79] November 2023: xAI: 314 Unknown Unknown: Apache 2.0 Used in Grok chatbot. Grok-1 has a context length of 8,192 tokens and has access to X (Twitter ...
DeepSeek (Chinese: 深度求索; pinyin: Shēndù Qiúsuǒ) is a Chinese artificial intelligence company that develops open-source large language models (LLMs). Based in Hangzhou, Zhejiang, it is owned and funded by Chinese hedge fund High-Flyer, whose co-founder, Liang Wenfeng, established the company in 2023 and serves as its CEO.
The largest models, such as Google's Gemini 1.5, presented in February 2024, can have a context window sized up to 1 million (context window of 10 million was also "successfully tested"). [45] Other models with large context windows includes Anthropic's Claude 2.1, with a context window of up to 200k tokens. [ 46 ]
Mistral AI was established in April 2023 by three French AI researchers: Arthur Mensch, Guillaume Lample and Timothée Lacroix. [17] Mensch, a former researcher at Google DeepMind, brought expertise in advanced AI systems, while Lample and Lacroix contributed their experience from Meta Platforms, [18] where they specialized in developing large-scale AI models.
Since the model relies on Query (Q), Key (K) and Value (V) matrices that come from the same source itself (i.e. the input sequence / context window), this eliminates the need for RNNs completely ensuring parallelizability for the architecture. This differs from the original form of the Attention mechanism introduced in 2014.
A prompt for a text-to-text language model can be a query, a command, or a longer statement including context, instructions, and conversation history. Prompt engineering may involve phrasing a query, specifying a style, choice of words and grammar, [ 3 ] providing relevant context, or describing a character for the AI to mimic.