Salesforce has released a new suite of AI-powered tools that can handle massive amous of textual data, up to 1.5 trillion words or tokens. Known as the XGen-7B family of models, these tools can be used specifically to handle unstructured data (data that does not fit neatly io rows and columns, such as text and images) and are much better analyzed and organized than LLAMA meta-models. Have.
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As more people start using AI tools like ChatGPT, the data fed io these systems will become more complex and structured. This complexity makes it more difficult to use tools like ChatGPT, which are designed to analyze language and text, when the input or data being analyzed does not follow a clear structure. Therefore, there is a growing need for advanced systems that can handle unstructured data and do more to meet the growing demand for artificial ielligence tools.
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Businesses can take advaage of chat systems like ChatGPT or BARD that can provide summaries of long documes or analyze customer data to gain insights. However, for these chat systems to be effective, they need to be trained on huge amous of data. Many businesses opt for smaller, cheaper models of these chat systems, which aren’t always capable of complex tasks like summarizing long documes or scrutinizing customer data. Therefore, since these models cannot handle such complex tasks well, these businesses cannot fully benefit from the benefits of this technology.
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Source game language models such as LLAMA, Falcon-7B, and meta MPT-7B are not ideal in managing texts or long documes, because they are not able to manage a large amou of texts and can only corol the maximum sequence length of about 2000 tokens or text units. However, the XGen-7B family of language models developed by Salesforce are trained using a technique called “standard dense atteion” and are therefore capable of processing much larger input data, up to 1.5 trillion tokens. . This has made the meioned language models an effective tool for managing and analyzing long documes.
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Salesforce researchers selected a set of linguistic models with seven billion parameters and trained them using a combination of Salesforce and JAXFORMER data, as well as publicly available training data. This model has achieved better results compared to open source models such as LLAMA, Falcon and Redpajama. The researchers also found that it costs only $150,000 to train a model with 1 trillion tokens using the Google Cloud Computing Platform TPU-V4, which is a more cost-effective and efficie way to train large language models. Thus, researchers have been able to create an advanced AI model that can analyze and process large amous of data more accurately than other open-source alternatives, while keeping the cost of training the model relatively low.
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