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Adapters-for-All: Nyun Zero enables building high-performant AI models through cost-effective fine-tuning

Background

With the growing size of AI models, full fine-tuning of pre-trained models has become increasingly expensive and infeasible. Multiple efficient fine-tuning methods have been proposed in the literature like LoRA which substantially bring down the cost of fine-tuning large models. Recent research has shown that not just large models but even smaller AI models like Resnets, Vision transformers, Yolos have better performance when fine-tuned using efficient fine-tuning methods. With this motivation, Nyun Zero has a brand new plugin that can help users save massive costs in fine-tuning any AI model while surpassing full-finetuning performance - Nyun Adapt!

Cutting Months to Days: Nyun Zero's Fast-Track Approach to AI Model Optimization

Background

In the ever-evolving landscape of AI model development, the conventional approach of AI model development has long been characterized by its laborious, time-intensive processes, often resulting in varied success rates and limited scalability. Nyun Zero is ready to bring a transformative shift, revolutionizing the way organizations approach model building and deployment. Nyun Zero offers a streamlined and automated solution, significantly reducing development time while consistently delivering high success rates. By leveraging advanced algorithms and automation, Nyun Zero empowers data scientists to navigate the complexities of model development with ease, ultimately accelerating AI initiatives and achieving superior results. Let's dive into a detailed comparison between the conventional approach and Nyun Zero to understand the profound impact this paradigm shift has on the model building to the deployment process.

No more Trade-offs on image resolution! Nyun Zero lets build AI models at gigapixel scale.

Background

In the ever-evolving field of computer vision, deep learning models have established themselves as the cornerstone of advanced feature extraction, surpassing traditional algorithms. However, as technology pushes the boundaries of data acquisition, AI practitioners are faced with a growing challenge: how to train deep learning models effectively on very large images. Large images are everywhere now, be it the medical imaging domain or the remote sensing survey. Thus, a solution is needed where the large deep learning images can be processed seamlessly.

Streamlining the Compression of Indic Language Models with NyunZero

Background

In the rapidly evolving landscape of AI, LLMs play a pivotal role in understanding and generating human-like text. OpenHathi, based on the impressive LLaMA-7B architecture, stands out as a powerful Indic language model. Leveraging its capabilities can significantly enhance natural language processing tasks in various applications. In this article, we will explore the seamless compression of OpenHathi with AWQ quantization and TensorRT-LLM engine conversion made possible through NyunZero.