Sign up to save your podcasts
Or
Share MiniMax M2.1 Bets That ‘Most Usable’ Beats ‘Most Massive’
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
MiniMax M2.1 Bets That ‘Most Usable’ Beats ‘Most Massive’
This story was originally published on HackerNoon at: https://hackernoon.com/minimax-m21-bets-that-most-usable-beats-most-massive.
LLMs are getting bigger, but most developers still have to work within tight limits. MiniMax M2.1 is an attempt to square that circle.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
You can also check exclusive content about #ai, #minimax-m2.1, #minimax, #chinese-ai-startup, #chinese-ai-startup-ipo, #minimax-m2, #ai-native-development, #ai-native-dev, and more.
This story was written by: @ainativedev. Learn more about this writer by checking @ainativedev's about page,
and for more stories, please visit hackernoon.com.
LLMs are getting bigger, but most developers still have to work within tight limits on speed, cost, and hardware. MiniMax M2.1 is an attempt to square that circle: a large model that behaves more like a much smaller one at inference time.
View all episodes
By HackerNoon
5
11 ratings
This story was originally published on HackerNoon at: https://hackernoon.com/minimax-m21-bets-that-most-usable-beats-most-massive.
LLMs are getting bigger, but most developers still have to work within tight limits. MiniMax M2.1 is an attempt to square that circle.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
You can also check exclusive content about #ai, #minimax-m2.1, #minimax, #chinese-ai-startup, #chinese-ai-startup-ipo, #minimax-m2, #ai-native-development, #ai-native-dev, and more.
This story was written by: @ainativedev. Learn more about this writer by checking @ainativedev's about page,
and for more stories, please visit hackernoon.com.
LLMs are getting bigger, but most developers still have to work within tight limits on speed, cost, and hardware. MiniMax M2.1 is an attempt to square that circle: a large model that behaves more like a much smaller one at inference time.