# Pre-trained Models

MILS is an open-source project released by Facebook Research, designed to demonstrate the capabilities of large language models (LLMs) in handling visual and auditory tasks without any prior training. This technology leverages pre-trained models and optimization algorithms to automatically generate descriptions for images, audio, and video. This breakthrough offers new insights into the development of multi-modal AI, showcasing the potential of LLMs in cross-modal tasks. The model is primarily targeted at researchers and developers, providing them with a powerful tool to explore multi-modal applications. Currently, this project is free and open-source, aimed at advancing academic research and technological development.

TimesFM is a pre-trained time series forecasting model developed by Google Research, intended for time series prediction tasks. The model has been pre-trained on multiple datasets and can handle time series data of varying frequencies and lengths. Its main advantages include high performance, strong scalability, and ease of use. The model is suitable for various applications requiring accurate time series data predictions in fields such as finance, meteorology, and energy. It is freely available on the Hugging Face platform, allowing users to easily download and utilize it.

SynCamMaster is an advanced video generation technology capable of synchronously producing multi-camera videos from diverse perspectives. This technology enhances dynamic consistency of video content from different angles through pre-trained text-to-video models, making it significantly relevant for applications like virtual shooting. Key advantages include the ability to generate open-world videos from any viewpoint, integration of 6 degrees of freedom camera poses, and a specially designed progressive training plan that utilizes multi-camera imagery and monocular video as supplements, greatly improving model performance.

ClearerVoice-Studio is an open-source AI-driven voice processing toolkit designed for researchers, developers, and end-users. It offers functionalities such as voice enhancement, separation, and target speaker extraction, along with the latest pre-trained models and training and inference scripts accessible through this repository. This toolkit is favored for its pre-trained models, ease of use, comprehensive features, and community-driven nature.

The allenai/olmo-mix-1124 dataset, provided by Hugging Face, is a large-scale multimodal pre-training dataset primarily used for training and optimizing natural language processing models. It contains a vast amount of textual information across multiple languages and can be applied to various text generation tasks. Its significance lies in providing a rich resource that enables researchers and developers to train more accurate and efficient language models, thus advancing the field of natural language processing.

Qwen2.5-Coder is the latest series of the Qwen large language model, focusing on code generation, reasoning, and fixing. Built on the powerful Qwen2.5 with an extended training dataset of 5.5 trillion tokens that includes source code, text code bases, and synthetic data, Qwen2.5-Coder-32B has become the leading open-source code LLM, matching GPT-4o in coding abilities. This model not only enhances coding capabilities but also maintains superiority in mathematics and general abilities, providing a comprehensive foundation for real-world applications like code assistance.

The Qwen2.5-Coder series consists of code-specific models based on the Qwen2.5 architecture, including Qwen2.5-Coder-1.5B and Qwen2.5-Coder-7B. These models continue to be pre-trained on a massive corpus of over 5.5 trillion tokens, showcasing impressive code generation capabilities while maintaining generality through meticulous data cleaning, scalable synthetic data generation, and balanced data mixing. Qwen2.5-Coder has achieved state-of-the-art performance in over ten benchmark tests across various code-related tasks, including code generation, completion, reasoning, and repair, consistently outperforming larger models of comparable size. The release of this series not only pushes the boundaries of intelligent coding research but also encourages developers to adopt it for real-world applications through its licensing.

Aya Expanse is an open-weight research model with advanced multilingual capabilities. It merges high-performance pre-trained models with one year of research from Cohere For AI, including data arbitrage, multilingual preference training, safety tuning, and model fusion. This powerful multilingual large language model serves 23 languages, including Arabic, Chinese (Simplified and Traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese.

MetaCLIP is an open-source machine learning model utilized for joint representation learning of images and text. It filters the CLIP data through a simple algorithm without dependence on previous model filters, thereby improving data quality and transparency. MetaCLIP's key contributions include filter-free data filtering, transparent training data distributions, scalable algorithms, and standardized CLIP training setups. The model emphasizes the importance of data quality and provides pre-trained models to support researchers and developers in conducting controlled experiments and fair comparisons.