With the rapid advancement of Artificial Intelligence, many open-source models have excelled, allowing companies and developers to experience and implement cutting-edge technologies without high costs. Let's explore some of the main open AI models and understand their specialties. One of the leading models is the **LLama (Large Language Model Meta AI)**, developed by Meta. This model stands out in text generation, covering simple to complex interactions. LLaMA offers several variants, with up to 65 billion parameters, allowing flexibility in various applications, from chatbots to recommendation systems. Another important open-source model is the **GPT-Neo**, developed by EleutherAI. It is an alternative to GPT-3 and is designed to function as a high-capacity text generator. With variants that have up to 2.7 billion parameters, GPT-Neo is a valuable resource for companies looking for natural language processing solutions without costs associated with commercial APIs. *Stable Diffusion** is an essential tool for image generation. Being open-source, it allows artists and developers to create text-based images. Its ability to create high-quality images with a wide range of styles makes it a popular option between creators and influencers. In relation to audio, the **Mozilla TTS (Text-to-Speech)** stands out as a robust open-source solution for text-to-speech conversion. It allows developers to customize voices and languages, being widely used in applications that require accessibility features. **DeepSeek-V3** is an advanced Mixture-of-Experts (MoE) language model with a total of 671 billion parameters, activating 37 billion for each token. To ensure efficient inference and economic training, the model uses the Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were validated in the previous version, DeepSeek-V2. An innovation of DeepSeek-V3 is the loss-free strategy for load balancing, as well as a multi-token prediction training goal that improves its performance. The model was pre-trained in 14.8 trillion different and high-quality tokens, followed by steps of Fine-Tuning Supervised and Learning by Reinforcement. Comprehensive reviews have shown that DeepSeek-V3 surpasses other open source models and achieves performance comparable to high-end closed models. Despite its high performance, the model requires only 2.788 million hours GPU H800 for training, displaying a stable process without irreversible peak loss or the need for rollback throughout the training. Finally, the **Haystack**, an open-source framework for building questions and answers systems and virtual assistants, has become a popular choice among companies to integrate AI features into their products.
