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DeepSeek-V3.2 released (huggingface.co)

submitted 3 weeks ago* (last edited 3 weeks ago) by yogthos@lemmygrad.ml to c/technology@hexbear.net

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DeepSeek V3.2 introduces Native Sparse Attention architecture designed to make long-context models more efficient without sacrificing performance. Instead of applying sparsity only during inference (after training is complete), NSA is designed to be sparse from the very beginning and is trainable from end-to-end.

By learning the sparse patterns during pretraining, DeepSeek is able to exceed the performance of standard Full Attention models in different benchmarks. It also allows the model to be efficiently fine-tuned for complex tasks like chain-of-thought reasoning.

There's a dramatic reduction in the number of token needed for both prefilling and decoding as the context length increases, making it much more economical to run.

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[–] BountifulEggnog@hexbear.net 18 points 3 weeks ago (1 children)

Massive cuts to their api pricing too:

[–] yogthos@lemmygrad.ml 11 points 3 weeks ago (1 children)

and it was already ridiculously cheap

[–] JoeByeThen@hexbear.net 5 points 3 weeks ago

But are they gonna narc on comrades trying to automate their catfish fisheries? thinkin-lenin

[–] AtmosphericRiversCuomo@hexbear.net 5 points 3 weeks ago (1 children)

Probably going to be great for shorter contexts. Longer remains to be seen.

[–] yogthos@lemmygrad.ml 7 points 3 weeks ago

The papers seem to suggest that it does handle long contexts well.