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Model weights for the first version of Llama were only available to researchers on a case-by-case basis, under a non-commercial license. [8] [3] Unauthorized copies of the first model were shared via BitTorrent. [9] Subsequent versions of Llama were made accessible outside academia and released under licenses that permitted some commercial use ...
Outperforms GPT-3.5 and Llama 2 70B on many benchmarks. [82] Mixture of experts model, with 12.9 billion parameters activated per token. [83] Mixtral 8x22B April 2024: Mistral AI: 141 Unknown Unknown: Apache 2.0 [84] DeepSeek LLM November 29, 2023: DeepSeek 67 2T tokens [85]: table 2 12,000}} DeepSeek License
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 ...
The architecture is essentially the same as Llama. DeepSeek LLM 29 Nov 2023 Base; Chat (with SFT) The architecture is essentially the same as Llama. DeepSeek-MoE 9 Jan 2024 Base; Chat Developed a variant of mixture of experts (MoE). DeepSeek-Math Apr 2024 Base Initialized with DS-Coder-Base-v1.5 Instruct (with SFT) RL (using a process reward model)
Notably, in the case of larger language models that predominantly employ sub-word tokenization, bits per token (BPT) emerges as a seemingly more appropriate measure. However, due to the variance in tokenization methods across different Large Language Models (LLMs), BPT does not serve as a reliable metric for comparative analysis among diverse ...
Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share. [1] These attributes extend to each of the system's components, including datasets, code, and model parameters, promoting a collaborative and transparent approach to AI development. [1]
Foundation models are inherently multi-purpose: to use these model for a specific use case requires some form of adaptation. At a minimum, models need to be adapted to perform the task of interest (task specification), but often better performance can be achieved by more extensive adaptation to the domain of interest (domain specialization).
Performance of AI models on various benchmarks from 1998 to 2024. In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down.