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memorizing-transformers-pytorch reviews and mentions
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A single API call using almost the whole 32k context window costs around 2$.
There is a GitHub repo https://github.com/lucidrains/memorizing-transformers-pytorch the implementation deviates from the paper slightly, using a hybrid attention across attention logits local and distant (rather than the sigmoid gate setup). It also uses cosine similarity attention (with learned temperature) for the KNN attention layer. There are also some features that are not mentioned in the paper, such as Transformer-XL memories and shifting memories down. There are no easy-to-use Memorizing Transformers implementations yet.
- You’ll be able to run chatgpt on your own device quite easily very soon
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[R] Memorizing Transformers - Google 2022
Github: https://github.com/lucidrains/memorizing-transformers-pytorch
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Memorizing Transformers – models that can acquire new knowledge immediately
have an implementation of this over at https://github.com/lucidrains/memorizing-transformers-pytorc..., for any researcher exploring retrieval and memory with attention networks
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A note from our sponsor - SaaSHub
www.saashub.com | 27 Apr 2024
Stats
Basic memorizing-transformers-pytorch repo stats
4
608
2.6
10 months ago
lucidrains/memorizing-transformers-pytorch is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of memorizing-transformers-pytorch is Python.
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