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  2. Llama (language model) - Wikipedia

    en.wikipedia.org/wiki/Llama_(language_model)

    Code Llama is a fine-tune of LLaMa 2 with code specific datasets. 7B, 13B, and 34B versions were released on August 24, 2023, with the 70B releasing on the January 29, 2024. [29] Starting with the foundation models from LLaMa 2, Meta AI would train an additional 500B tokens of code datasets, before an additional 20B token of long-context data ...

  3. DeepSeek - Wikipedia

    en.wikipedia.org/wiki/DeepSeek

    The DeepSeek-LLM series was released in November 2023. It has 7B and 67B parameters in both Base and Chat forms. DeepSeek's accompanying paper claimed benchmark results higher than Llama 2 and most open-source LLMs at the time. [29]: section 5 The model code is under the source-available DeepSeek License. [50]

  4. Hugging Face - Wikipedia

    en.wikipedia.org/wiki/Hugging_Face

    Hugging Face, Inc. is an American company incorporated under the Delaware General Corporation Law [1] and based in New York City that develops computation tools for building applications using machine learning.

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  6. Google Play - Wikipedia

    en.wikipedia.org/wiki/Google_Play

    Google Play Store applications are self-contained Android Package files (APK), similar to .exe files used to install programs on Microsoft Windows computers. [120] On Android devices, an "Unknown sources" feature in Settings allows users to bypass the Google Play Store and install APKs from other sources. [121]

  7. llama.cpp - Wikipedia

    en.wikipedia.org/wiki/Llama.cpp

    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.

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  9. OpenAI o3 - Wikipedia

    en.wikipedia.org/wiki/OpenAI_o3

    Reinforcement learning was used to teach o3 to "think" before generating answers, using what OpenAI refers to as a "private chain of thought". [10] This approach enables the model to plan ahead and reason through tasks, performing a series of intermediate reasoning steps to assist in solving the problem, at the cost of additional computing power and increased latency of responses.