technology

24053 readers

222 users here now

On the road to fully automated luxury gay space communism.

Spreading Linux propaganda since 2020

Rules:

1. Obviously abide by the sitewide code of conduct. Bigotry will be met with an immediate ban
2. This community is about technology. Offtopic is permitted as long as it is kept in the comment sections
3. Although this is not /c/libre, FOSS related posting is tolerated, and even welcome in the case of effort posts
4. We believe technology should be liberating. As such, avoid promoting proprietary and/or bourgeois technology
5. Explanatory posts to correct the potential mistakes a comrade made in a post of their own are allowed, as long as they remain respectful
6. No crypto (Bitcoin, NFT, etc.) speculation, unless it is purely informative and not too cringe
7. Absolutely no tech bro shit. If you have a good opinion of Silicon Valley billionaires please manifest yourself so we can ban you.

founded 5 years ago

MODERATORS

context@hexbear.net

SexUnderSocialism@hexbear.net

gaycomputeruser@hexbear.net

Wakmrow@hexbear.net

DeepSeek-V3.2 released (huggingface.co)

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

5 comments fedilink hide all child comments

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.

you are viewing a single comment's thread
view the rest of the comments

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

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