LLMs 的一个关键限制是它们无法处理比其训练的 context window 更长的序列。由于低效的内存管理和不断增长的注意力计算成本,大多数模型在面对扩展输入时性能会下降。现有的解决方案通常依赖于 fine-tuning,这需要大量资源并且需要高质量的长上下文数据集。如果没有有效的上下文扩展方法,诸如文档摘要、retrieval-augmented...
《LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models》 啄木鸟:论文简读:LongLoRA NLP自然语言处理:中大&MIT | 提出LongLoRA,微调LLaMA2,Token从4K扩展至100K 《LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens》 白强伟:【自然语言处理】【大模型】VeRA:可调参数比LoRA小10...
A few weeks ago we talked abouttoken limitson LLM chat APIs and how this prevents an infinite amount of history being remembered as context. Asliding windowcan limit the overall context size, and making the sliding windowmore efficientcan help maximize the amount of context sent with each new ...
ValleyTalk brings infinite dialogue to Stardew Valley. Covering all villagers, the mod uses AI to create fully context aware dialogue for anywhere repetition is a problem, leaving classic lines in pl
RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free
Collect all previous context info, so later tokens can use it. This is always hard. The shifted channels can focus on (2), so we have good propagation of info. It's like some kind of residual connection, or a small RNN inside the transformer. You can use token-shift in usual QKV ...
In this tutorial, we discuss and show how to run MemGPT - an LLM with the potential for infinite context understanding.
The last few posts have been about the different ways to create an ‘infinite chat’, where the conversation between the user and an LLM model is not limited by the token size limit and as much historical context as possible can be used to answer future queries. We previously covered: ...
1 引言 2 方法 2.1 Infini-attention 2.1.1 缩放点积注意力 2.1.2 压缩式内存(Compressive Memory) 2.2 内存和有效上下文窗口(Memory and Effective Context Window) 3 实验 3.1 长上下文语言建模 3.2 LLM 持续预训练 4 相关工作 5 结论 References 附录 A 额外的训练细节 B 口令(passkey)检索任务图片...
对此,我们换个说法来理解就是,假设每次都考虑大小为n的context window,那么这些context window的信息在每次自回归都融合进入initial token,那么即使后续丢弃掉这些token,它们的信息仍然会保留在initial token中。 3.2 Method StreamingLLM的做法类似于LM-Infinite,它们两者唯一的区别在于位置编码的不一样。StreamingLLM的...