Boundaryfree
@boundaryfree.bsky.social
To see a World in a Grain of Sand
And a Heaven in a Wild Flower
Hold Infinity in the palm of your hand
And Eternity in an hour
And a Heaven in a Wild Flower
Hold Infinity in the palm of your hand
And Eternity in an hour
7/ Overall, DeepSeek is a glimpse into the future of AI: smarter, faster, and more resource-conscious. It’s not just about building bigger models anymore—it’s about building better ones. Thoughts? 💡
January 28, 2025 at 7:36 AM
7/ Overall, DeepSeek is a glimpse into the future of AI: smarter, faster, and more resource-conscious. It’s not just about building bigger models anymore—it’s about building better ones. Thoughts? 💡
6/ However, as with any tool, there’s a tradeoff: in unknown domains, where the model needs to explore and define new "experts," DeepSeek may face challenges. Its strength lies in efficiency, not necessarily adaptability to the completely novel.
January 28, 2025 at 7:36 AM
6/ However, as with any tool, there’s a tradeoff: in unknown domains, where the model needs to explore and define new "experts," DeepSeek may face challenges. Its strength lies in efficiency, not necessarily adaptability to the completely novel.
5/ These approaches are game-changers, especially for tasks in familiar domains where the right "experts" and rules can guide the model efficiently. Fewer resources, fewer iterations, faster results. 🚀
January 28, 2025 at 7:36 AM
5/ These approaches are game-changers, especially for tasks in familiar domains where the right "experts" and rules can guide the model efficiently. Fewer resources, fewer iterations, faster results. 🚀
4/ Internal Reinforcement Learning:
What’s really unique is DeepSeek’s self-reliance. Instead of relying on an external “critic” to correct its outputs, it uses internal rules to self-assess and improve. It’s like giving the model a conscience!
What’s really unique is DeepSeek’s self-reliance. Instead of relying on an external “critic” to correct its outputs, it uses internal rules to self-assess and improve. It’s like giving the model a conscience!
January 28, 2025 at 7:36 AM
4/ Internal Reinforcement Learning:
What’s really unique is DeepSeek’s self-reliance. Instead of relying on an external “critic” to correct its outputs, it uses internal rules to self-assess and improve. It’s like giving the model a conscience!
What’s really unique is DeepSeek’s self-reliance. Instead of relying on an external “critic” to correct its outputs, it uses internal rules to self-assess and improve. It’s like giving the model a conscience!
3/ Multi-Head Latent Attention:
This innovation tackles a major inefficiency in how models generate text. By predicting multiple words at once instead of one-by-one, DeepSeek accelerates inference dramatically. Less waiting, more results!
This innovation tackles a major inefficiency in how models generate text. By predicting multiple words at once instead of one-by-one, DeepSeek accelerates inference dramatically. Less waiting, more results!
January 28, 2025 at 7:36 AM
3/ Multi-Head Latent Attention:
This innovation tackles a major inefficiency in how models generate text. By predicting multiple words at once instead of one-by-one, DeepSeek accelerates inference dramatically. Less waiting, more results!
This innovation tackles a major inefficiency in how models generate text. By predicting multiple words at once instead of one-by-one, DeepSeek accelerates inference dramatically. Less waiting, more results!
2/ Mixture of Experts Architecture:
DeepSeek doesn’t just brute-force its way through problems like traditional models. Instead, it activates only the relevant parameters—think tens of billions, not hundreds—saving massive compute costs. A true case of "work smarter, not harder."
DeepSeek doesn’t just brute-force its way through problems like traditional models. Instead, it activates only the relevant parameters—think tens of billions, not hundreds—saving massive compute costs. A true case of "work smarter, not harder."
January 28, 2025 at 7:36 AM
2/ Mixture of Experts Architecture:
DeepSeek doesn’t just brute-force its way through problems like traditional models. Instead, it activates only the relevant parameters—think tens of billions, not hundreds—saving massive compute costs. A true case of "work smarter, not harder."
DeepSeek doesn’t just brute-force its way through problems like traditional models. Instead, it activates only the relevant parameters—think tens of billions, not hundreds—saving massive compute costs. A true case of "work smarter, not harder."