Recurrent neural networks (RNNs) are a type of artificial intelligence primarily used in the field of deep learning. Unlike traditional neural networks, RNNs have memory that captures information about what has been computed so far. In other words, they use their understanding from previous inputs to influence the outputs they produce.
RNNs are called “recurrent” because they perform the same task on every element in a sequence and the output depends on previous computations. RNNs are still used to power smart technologies like Apple's Siri and Google Translate.
However, with the advent of transformers like ChatGPT, the natural language processing (NLP) landscape has changed. Transformers revolutionized his NLP tasks, but the memory and computational complexity of transformers increased quadratically with sequence length and required more resources.
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Now, a new open source project, RWKV, offers a promising solution to the GPU power conundrum. This project, supported by the Linux Foundation, aims to significantly reduce the computing requirements of GPT-level language learning models (LLMs), in some cases by up to a factor of 100.
Although RNNs exhibit linear scaling in memory and computational requirements, they have limited parallelization and scalability, making it difficult to match the performance of transformers. This is where RWKV comes into play.
RWKV (Receptance Weighted Key Value) is a new model architecture that combines the parallelizable training efficiency of transformers with the efficient inference of RNNs. result? Models that require significantly fewer resources (such as VRAM, CPU, and GPU) to run and train while maintaining high-quality performance. It also scales linearly with the length of the context and is generally suitable for training in languages other than English.
Despite these promising features, the RWKV model is not without its challenges. It is sensitive to prompt formatting and weak to tasks that require reflection. However, these issues are being resolved, and the potential benefits of this model far outweigh its current limitations.
The implications of the RWKV project are serious. Instead of requiring 100 GPUs to train an LLM model, RWKV models can provide similar results with less than 10 GPUs. This not only makes the technology more accessible, but also opens up possibilities for further advancements.