RapidFire AI revolutionizes Computer Vision model development enabling rapid iteration across diverse model architectures, transfer learning schemes, image transforms, video chunking, and quantization schemes to identify optimal accuracy-performance tradeoffs no matter the dataset size.
RapidFire AI revolutionizes Time and Event Series modeling by enabling rapid iteration across diverse sequence model architectures, windowing schemes, and temporal sampling schemes to quickly identify the best models even on the largest datasets. By streamlining the comparison process, teams can right-size the models for inference constraints, while keeping the models up to date more frequently.
NLP/LLMs
RapidFire AI elevates LLM/NLP applications by accelerating LLM/VLM fine-tuning (including LoRA), continued pre-training, and RL-based post-training (including DPO and GRPO), as well as regular training, fine-tuning, and transfer learning for older Attention-based BERT or BART style models. This enables teams to deploy more customized and accurate large or small language models with significantly reduced development cycles, inference costs, and hallucinations.
Multimodal
RapidFire AI is a natively multimodal AI system that can support any combination of data modalities being present in a single example: images, video, text, code, time/event series, tabular, semistructured, etc. This enables teams to be more nimble in mixing and matching pre-trained model components, exploring diverse model architecture, dropping subsets of input sources, and altering input or output representations as they see fit to achieve the best balance of desired accuracy, costs, and latency.
