TinyLlama Reviews

DeepSeek-V2, developed by DeepSeek-AI, is a cutting-edge Mixture-of-Experts (MoE) language model designed for cost-effective training and high-speed inference. Boasting a massive 236 billion parameters—though only 21 billion are active per token—it efficiently handles a context length of up to 128K tokens. The model leverages advanced architectural innovations such as Multi-head Latent Attention (MLA) to optimize inference by compressing the Key-Value (KV) cache and DeepSeekMoE to enable economical training via sparse computation. Compared to its predecessor, DeepSeek 67B, it slashes training costs by 42.5%, shrinks the KV cache by 93.3%, and boosts generation throughput by 5.76 times. Trained on a vast 8.1 trillion token dataset, DeepSeek-V2 excels in natural language understanding, programming, and complex reasoning, positioning itself as a premier choice in the open-source AI landscape.

The next generation of the large language model. This release includes modelweights and starting code to pretrained and fine tuned Llama languages models, ranging from 7B-70B parameters. Llama 1 models have a context length of 2 trillion tokens. Llama 2 models have a context length double that of Llama 1. The fine-tuned Llama 2 models have been trained using over 1,000,000 human annotations. Llama 2, a new open-source language model, outperforms many other open-source language models in external benchmarks. These include tests of reasoning, coding and proficiency, as well as knowledge tests. Llama 2 has been pre-trained using publicly available online data sources. Llama-2 chat, a fine-tuned version of the model, is based on publicly available instruction datasets, and more than 1 million human annotations. We have a wide range of supporters in the world who are committed to our open approach for today's AI. These companies have provided early feedback and have expressed excitement to build with Llama 2