Foundation Models

Foundation model challenges

Enterprises face distinct challenges when it comes to foundation models. Their massive size makes them a costly and complex burden to host in-house, and adopting off-the-shelf FMs for production could lead to inadequate performance or significant governance and compliance issues.

Bridging the gap aims to overcome these obstacles by bridging the gap between foundation models and real-world enterprise applications for AI innovators. We assist enterprises in enhancing open-source models that are readily available, providing them with greater flexibility and options as they build AI solutions.

With’s platform, users can customize foundation models to fit their specific needs. The application development process starts with examining the predictions made by a chosen foundation model “out of the box” on their data. These initial predictions serve as training labels for the data points. assists users in identifying error modes in the model and efficiently addressing them through human labeling. This may involve updating the training labels using heuristics or prompts. The original foundation model can then be fine-tuned using the updated labels and evaluated again. This iterative “detect and correct” process continues until the adapted foundation model reaches the desired level of quality for deployment.

Fine Tuning Large Language Models with Human Guidance

Fine-tuning large language foundation models with a diverse global network of targeted humans involves leveraging the expertise of individuals from various backgrounds and cultures to improve the performance of NLP models. This approach ensures that models are effective across different languages, dialects, and regions.

  • Improved accuracy and effectiveness across different languages and regions. By involving individuals who are familiar with local dialects and expressions, the model can be fine-tuned to recognize and interpret nuances that might be missed by traditional training methods.
  • Cost-effective compared to traditional data collection and validation methods, which are time-consuming and expensive. To allocate teams from different countries and manage them, adds up to vast expenses and huge operational overhead.
  • Incorporating diverse perspectives into model development can increase its inclusivity and avoid bias. With, organizations ensure that their models are representative of different cultures and communities, and not biased towards a particular group.

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