Ring-1T-2.5 rebuilds Ant's trillion-parameter reasoner around linear attention
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Ring-1T-2.5 is released📣🚀with high efficiency in planning and multi-step tool collaboration. 🔧Try out and explore how this 1:7 MLA + Lightning Linear Attention boosts reasoning speed and exploration! #OpenSource #LRM 🤗 Hugging Face: https://t.co/np9ooMGhKY 📷 ModelScope: https://t.co/VirARspLJs
Context
A model built for multi-step planning and needs to process long chains of reasoning quickly, which is where the standard in most transformers becomes expensive. Ant Open Source's Ring-1T-2.5 mixes multi-head latent attention with a lightning linear-attention variant in a 1:7 ratio, which Ant says improves efficiency in planning and multi-step tool collaboration. It's a trillion-parameter , with weights published on Hugging Face and ModelScope.
- tool use — A model's ability to call external functions — run code, search the web, edit files — instead of only generating text.
- attention — The mechanism that lets a model weigh which earlier words matter for the word it's currently processing — the core operation of a transformer.
- reasoning model — A model trained to think — generating extended internal reasoning before answering — trading time and tokens for accuracy on hard problems.
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