Main idea of the model: to combine efficiency and scale of reasoning in one architecture.
🟠 Why it really matters?
- Total parameters: 1 trillion, of which ≈ 50 billion are active per token (MoE architecture).
- Trained on 20 trillion+ tokens, specially selected for logical thinking and reasoning tasks.
Context: 128,000 tokens.
Inside Evo-CoT (Evolutionary Chain of Thought) and Linguistics-Unit RL - new training methods for scalable reasoning.
Ling-1T is positioned as a model balancing speed and accuracy of responses.
The model demonstrates strong results in code, math, logic, and frontend generation tasks.
The architecture involves Mixture-of-Experts (1/32 activation), MTP layers, and expert routing.
Ling-1T shows that huge models can be made not only powerful but also efficient.
HF
#Ling1T #AI #ML #OpenSource #Reasoning #TrillionScale #FP8
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