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Llama (Large Language Model Meta AI, formerly stylized as LLaMA) is a family of autoregressive 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]
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 ]
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). [80] Gemini 1.0: December 2023: Google DeepMind: Unknown Unknown Unknown: Proprietary Multimodal ...
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.
DeepSeek (Chinese: 深度求索; pinyin: Shēndù Qiúsuǒ) is a Chinese artificial intelligence company that develops open-source large language models (LLM). Based in Hangzhou, Zhejiang, it is owned and solely funded by Chinese hedge fund High-Flyer, whose co-founder, Liang Wenfeng, established the company in 2023 and serves as its CEO.
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.
Claude is a family of large language models developed by Anthropic. [1] [2] The first model was released in March 2023.The Claude 3 family, released in March 2024, consists of three models: Haiku optimized for speed, Sonnet balancing capabilities and performance, and Opus designed for complex reasoning tasks.
ALiBi allows pretraining on short context windows, then fine-tuning on longer context windows. Since it is directly plugged into the attention mechanism, it can be combined with any positional encoder that is plugged into the "bottom" of the entire network (which is where the sinusoidal encoder on the original transformer, as well as RoPE and ...