CLIP4Clip模型中，输入向量维度增大使Transformer计算量加倍，特别是视频特征提取时重算序列降低效率，成为...
此外，RWKV的设计灵感来源于Transformer，但它是100%无注意力机制的，这种设计简化了模型结构，可能有助...
RWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). And it's 100% attention-free. You only need the hidden state at position t to compute the state at position t+1. You can use the "GPT" mode to quickly ...
由于 RWKV 是 RNN,因此很自然地认为它在基准测试中的表现不如 transformer。此外,这听起来像是线性注意力。许多以前的线性时间注意力Transformer架构(如“Linformer”、“Nystromformer”、“Longformer”、“Performer”)似乎都没有SOTA。 # 性能 RWKV 似乎可以像 SOTA transformer一样缩放。至少多达140亿个参数。 # ...
RWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). And it's 100% attention-free. You only need the hidden state at position t to compute the state at position t+1. You can use the "GPT" mode to quickly ...
Hello today we're going to look at RWKV, which in its own words is reinventing RNN for the Transformer era。 This is a very interesting project and very interesting model architecture because it has some properties of transformers。 notably it's a model architecture that's very scalable in ...
TLDR vs Existing transformer models Good Lower resource usage (VRAM, CPU, GPU, etc) when running and training. 10x to a 100x lower compute requirementscompared to transformers with large context sizes. Scales to any context length linearly (transformers scales quadratically) ...
RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), ...
RWKV是一种挑战Transformer的新架构,基于RNN设计,复杂度更低,生成内容速度快,且显存占用恒定。RWKV背后团队来自多家机构,由30位研究人员组成。RWKV近期推出新架构模型RWKV-5和RWKV-6,进一步提升了模型的表达能力,并引入了一个1.12万亿token的多语言语料库,强化了多语言支持。
# RWKV 模型是一种巧妙的 RNN 架构,使其能够像transformer一样进行训练。所以要解释RWKV,我需要先解释一下RNNs和transformers。 # RNN 循环神经网络 传统上,用于序列(如文本)处理的神经网络是 RNN(如 LSTM)。RNN 接受两个输入:State和Token。它一次通过输入序列一个Token,每个Token更新状态。例如,我们可以使用 ...