emozilla, 2023. Dynamically Scaled RoPE further increases performance of long context LLaMA with zero fine-tuning.link Peng et al, 2023. YaRN: Efficient Context Window Extension of Large Language Models.link Press et al, 2022. Train Short, Test Long: Attention with linearbiasesenables input lengt...
受限于平方复杂度的缘由,预训练的LLM采用了有限的context window。那么,问题来到了第二步——训练好的模型是否具备良好的外推性以做到“Train Short, Test Long”?答案则相当程度上取决于Transformer采用的位置编码。 3.1. 主流位置编码 在笔者之前写的Pikachu5808:Transformer中的各种改进中,针对位置编码已做了简要描述...
Large context window is a desirable feature in large language models (LLMs). However, due to high fine-tuning costs, scarcity of long texts, and catastrophic values introduced by new token positions, current extended context windows are limited to around 128k tokens. This paper ...
《Training-Free Long-Context Scaling of Large Language Models》翻译与解读 Abstract The ability of Large Language Models (LLMs) to process and generate coherent text is markedly weakened when the number of input tokens exceeds their pretraining length. Given the expensive overhead of finetuning larg...
1. 权重平均和模型融合可将多个 LLM 组合成单个更好的模型,并且这个新模型还没有传统集成方法的典型缺陷,比如更高的资源需求。 2. 代理调优(proxy-tuning)技术可通过使用两个小型 LLM 来提升已有大型 LLM 的性能,这个过程无需改变大模型的权重。 3. 通过将多个小型模块组合起来创建混合专家模型,可让所得 LLM ...
Context window:128,000 Access:API OpenAI's Generative Pre-trained Transformer (GPT) models kickstarted the latest AI hype cycle. There are two main models currently available:GPT-4o and GPT-4o mini. Both are also multimodal models, so they can also handle images and audio. All the differen...
Long-Context下LLM模型架构全面介绍 缓存架构模型内存LLM 随着ChatGPT的快速发展,基于Transformer的大型语言模型(LLM)为人工通用智能(AGI)铺平了一条革命性的道路,并已应用于知识库、人机界面和动态代理等不同领域。然而,存在一个普遍的限制:当前许多LLM受资源限制,主要是在较短的文本上进行预训练,使它们对现实世界中...
LongLoRA是港中文大学和MIT联合发出的论文《LONGLORA:EFFICIENT FINE-TUNING OF LONGCONTEXT LARGE LANGUAGE MODELS》提出的方法,本论文的主要改进之处在于: 1. 基于位置插值方法,在上下文扩展任务中引入LoRA方法,降低对硬件资源的专需。 2. 提出了shift
但是如果 inference 成本和延时没有降到足够低,大家在使用 long-context 方面还是会有不少顾虑。而且在 long-context 情况下,可能进一步增加了大家对于强大推理能力的期待。越大的 context 代表的是越复杂越端到端的应用方式,如果推理能力没跟上,那么长的 context 好像用处也不大。
Long-context LLM inference faces two major challenges: 1) long pre-filling stage attention latency, and 2) high storage and transfer costs for KV cache. Previous efficient methods for long-context LLMs have focused on KV-cache compression, static sparse attention (e.g...