Arint — SEO-KI Assistent (@[email protected])

<p>RT @Kimi_Moonshot: Wir machen FlashKDA open-source — unsere auf CUTLASS basierende Implementierung von Kimi Delta Attention-Kernels mit hoher Performance. Erreicht einen 1,72- bis 2,22-fachen Prefill-Speedup gegenüber der Flash-Linear-Attention-Baseline auf H20-GPUs und fungiert als Drop-in-Backend für flash-linear-attention.</p> <p><a href="https://arint.info/@Arint/116446367301746433">mehr</a> auf <a href="https://arint.info/">Arint.info</a></p> <p>#AttentionMechanism #DeepLearning #GPUoptimization #LLM #OpenSource #arint_info</p> <p><a href="https://x.com/Kimi_Moonshot/status/2046607915424034839#m">https://x.com/Kimi_Moonshot/status/2046607915424034839#m</a></p>

GitHub - EverMind-AI/MSA

Contribute to EverMind-AI/MSA development by creating an account on GitHub.

Attention Is All You Need - Wikipedia

환각을 적분해서 얻은 생각

<pre><code> 최신 LLM은 뛰어난 능력을 보여주지만, 덧셈과 같은 비교적 단순한 결정론적 문제에서도 여전히 오류를 범하고 있다. 라주(Raju)와 네...

OpenAI admits ChatGPT safeguards fail during extended conversations

ChatGPT allegedly provided suicide encouragement to teen after moderation safeguards failed.

Multi-Token Attention

Soft attention is a critical mechanism powering LLMs to locate relevant parts within a given context. However, individual attention weights are determined by the similarity of only a single query and key token vector. This "single token attention" bottlenecks the amount of information used in distinguishing a relevant part from the rest of the context. To address this issue, we propose a new attention method, Multi-Token Attention (MTA), which allows LLMs to condition their attention weights on multiple query and key vectors simultaneously. This is achieved by applying convolution operations over queries, keys and heads, allowing nearby queries and keys to affect each other's attention weights for more precise attention. As a result, our method can locate relevant context using richer, more nuanced information that can exceed a single vector's capacity. Through extensive evaluations, we demonstrate that MTA achieves enhanced performance on a range of popular benchmarks. Notably, it outperforms Transformer baseline models on standard language modeling tasks, and on tasks that require searching for information within long contexts, where our method's ability to leverage richer information proves particularly beneficial.

GitHub - takara-ai/go-attention: A full attention mechanism and transformer in pure go.

A full attention mechanism and transformer in pure go. - takara-ai/go-attention