Playing around with ChatGPT is really fun. And can eat up your afternoon if you aren’t careful! However, the value of ChatGPT (and other public applications available) is limited for organizational use, as the models aren’t built or paired with your own business data.
The way forward to maximize the value of these large language models (LLMs) is to combine and train on your own data, based on the use case. This all starts with a strong data management strategy to ensure the quality of your data, and then ladders down into planning out the complex process of customizing the model—as most organizations’ use cases require combining domain-specific information from their vertical into a pre-existing large language model.
Today’s announcement builds on our continuing generative AI (GenAI) strategy and what we released on July 31, where we’re now expanding our portfolio of GenAI solutions with a new Dell Validated Design with NVIDIA for Model Customization and new professional services across strategy, consulting and data preparation, as well as managed services for the NVIDIA software stack.
Why Should I Customize a Pre-Trained Model?
One of the strengths of large language models (LLMs) is they contain a broad amount of information and knowledge, thanks to the substantial amount of text data used to train them. However, this also means the models often struggle to maintain accuracy on topics or items that were not used within the initial training dataset, which is why it’s so important to fine-tune models with your own proprietary data.
Pre-trained model customization is the process of retraining an existing generative AI model for task-specific or domain-specific use cases. This is often more efficient than to train the model from scratch on a new dataset.
This layered training approach—in which specialized information is added to a pre-trained model—is called transfer learning or model fine-tuning. This process creates application-specific parameters on top of pre-trained large language models, with the purpose of making the models perform better.
Standard customization techniques used today include transfer learning, fine-tuning, instruction tuning, prompt learning (including prompt tuning and P-tuning) and reinforcement learning with human feedback for example.
How to Avoid Design and Planning Pitfalls
The Dell Validated Design for Generative AI now supports both model customization and tuning, as well as inferencing, enabling a simpler implementation of GenAI models. The reference architecture delivers a documented architecture for the Dell PowerEdge XE9680 and PowerEdge XE8640 servers—all with a choice of NVIDIA Tensor Core GPUs—running NVIDIA AI Enterprise software, NVIDIA NeMo™ framework and Dell software.
Compute power is combined with storage options, such as Dell PowerScale F600 and Dell ObjectScale, with robust external storage capabilities that offer the scale and speed needed for operationalizing GenAI models. These storage platforms provide a foundational component across the lifecycle of an application, allowing customers to bring AI to their data and hosting multiple data types, from structured file and block to unstructured data types. Data types will become more important as organizations develop use cases that extract value and new outcomes from non-text models, including graphics, video, audio and non-Romanic language conversion.
The Dell infrastructure components are available with flexible consumption models via Dell APEX.
New Professional Services Helping Customers Deliver Their Vision Quicker
To quickly put the power of the Dell Validated Design for Generative AI to work, you can now take advantage of expanded implementation services to deploy the validated design, using the best practices documented in the new design guide. These new services complement the previously announced implementation services for the first validated design focused on GenAI inferencing.
If you are seeking Dell’s help in managing your GenAI environment, you can also take advantage of new Managed Services for Generative AI. With the benefit of these new managed services, you can focus your attention on customizing and tailoring models while Dell managed services experts take care of operating the GenAI infrastructure, simplifying your GenAI operations. These managed services are also aligned to the Dell Validated Design for Generative AI, managing the same solution software and hardware stack.
For organizations looking for help integrating data to GenAI models, you can now use new Data Preparation consulting services to leverage Dell’s experience with data analytics and IT strategy, recently recognized by Forbes. With these services, you can define GenAI data requirements, implement data pipelines to tag, cleanse, label and anonymize your data and populate LLM databases, such as vector databases.
Get Started Now with a Fee-Waived Workshop
With all the excitement about GenAI, one of the most pressing challenges for IT organizations is how to prioritize the many use cases that are bubbling up across their organizations. In our experience, the best decisions come by gaining consensus from business and IT stakeholders. This is one of the key outcomes of GenAI strategy services, recently updated to include an assessment of GenAI readiness across key dimensions (e.g., data, use cases, people). We also have an additional strategy offering: a half-day fee-waived Accelerator Workshop to help you accelerate your GenAI strategy.
IT organizations regularly report that one of their greatest generative AI challenges is a shortage of skills. To address this need, new Education Services for Generative AI now offer a wide range of GenAI training courses, including topics such as data engineering, large language model deployment and customization and NVIDIA hardware and software administration.
Embarking on the Next Phase of the GenAI Journey
Organizations are getting ready for GenAI outcomes. With powerful GenAI solutions from Dell Technologies and NVIDIA, you can now accelerate your digital transformation and start delivering value today.
Learn more here.
