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Fine Tuning LLMs

THE PROBLEM

LLMs are initially trained on vast and diverse datasets to learn the general structure and usage of text and language. This pre-training phase helps the model understand grammar, context, facts about the world, reasoning abilities, and more. Fine-tuning involves further training the pre-trained LLM on a smaller, more specific dataset to make it perform better on specific tasks or domains. This process adjusts the model weights slightly to better capture the nuances of the new data.


OUR SOLUTION

  • ZNZ Data Connect can connect to internal, disparate data sources to grab hold of an organization’s entire data estate at lightning speed (8M files/hour) and exabyte scale. 

  • ZNZ Data Detect identifies, enriches, and prepares the data for complete viewership and LLM injection. 

  • ZNZ Data Optic assists in fine tuning an LLM through its elastic indexing search database capabilities, which can help comb through data and provide more accurate and contextually relevant LLM responses. 

  • ZNZ Data Router then takes the packaged/prepared data and can route it to the relevant LLM models depending on an organization's policy, compliance, utilization, and security demands.


ZNZ Data Processing ensures seamless integration and access to diverse internal data sources, automates data collection and preprocessing to ensure that the training data is clean, relevant, and actionable, provides a unified data optic wherein stakeholders can implement mechanisms to track and manage data ownership and permissions and allow for clear visibility into who owns specific datasets, and routes data efficiently between various systems and workflows.


Other organizations and products focus on providing libraries and APIs that allow fine-tuning of LLMs. None of them, however, tackle an organization’s internal unstructured data estate. Therefore, other products are still leaving vital and mission-critical data in the dark and building LLMs using incomplete data. Our product is the only product that connects to all unstructured data sets across an organization’s data estate to provide complete and actionable data packages.

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