The 01.AI research team has introduced the Yi model family of Artificial Intelligence (AI) designed to bridge the gap between human language and visual perception. Uniquely, this model doesn’t simply parse text or images individually; it combines both, demonstrating an unprecedented degree of multi-modal understanding. This ground-breaking technology’s purpose is to mirror and extend human cognitive abilities.
Unlike earlier models that required improvement when tasked with understanding the context of large text sections or deriving meaning from text and visual cues, the Yi model family includes language-specific models that can process visual information alongside the text. This development is facilitated by a refined transformer that’s fine-tuned for data quality, enhancing performance standards across numerous benchmarks. Layered model building and training also contribute to this model’s success, along with a rigorous filtration process to ensure the data used for training is of high quality.
One notable achievement within the Yi model family is the Yi-9 B model, built using a novel two-stage training methodology. Using an extensive dataset of around 800 billion tokens, the model’s development process focused on parsing current data collection and selection, improving the model’s understanding and coding-related task performance. This resulted in considerable performance gains across a variety of benchmarks, including reasoning, knowledge, coding, and mathematics.
The Yi model family isn’t simply a theoretical tool but rather has practical applications. Its core strengths rest within the balance of data quantity and quality, and its fine-tuning process. The Yi-34B model, for example, is comparable to GPT-3.5 but with the additional advantage of being operatable on consumer-grade devices, thanks to effective quantization strategies. This makes it a beneficial tool for a range of applications, such as natural language processing and visual computing tasks.
A particularly exciting attribute of the Yi series is its capability to handle vision-language tasks. By combining the chat language model with a vision transformer encoder, it can interpret visual inputs with linguistic semantics. This capability enables it to understand and respond to multi-modal inputs of images and text, creating a wealth of possibilities for AI applications.
In conclusion, the development of the Yi model family is a substantial progression toward creating AI that can understand human language and sight complexities. Achieved using a refined transformer architecture and a revolutionary data processing approach, the creation of language and sight models has enabled understanding of multi-modal inputs. The models have performed exceptionally well across user preference evaluations and standard benchmarks and show promise for a variety of applications. For those wanting to learn more about the Yi model family, 01.AI has published a paper detailing its research, which is available online.
