What is Ling-1T
Ling-1T is a trillion-parameter language model open-sourced by Ant Group, positioned as a "flagship non-thinking model." Based on the MoE (Mixture of Experts) architecture, it has 1 trillion parameters, activates about 51 billion parameters per inference, supports a 128K context length, and is suitable for long document tasks. Ling-1T focuses on directly delivering high-quality reasoning results under a limited number of output tokens, with extremely high inference efficiency. The model performs excellently in tasks such as programming, mathematical reasoning, knowledge understanding, and creative writing, and is in the first tier of open-source models.
Main Features of Ling-1T
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Efficient Reasoning: Directly delivers high-quality reasoning results under a limited number of output tokens, with extremely high inference efficiency, suitable for quickly solving problems.
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Long Text Processing: Supports a 128K context length, can handle long document tasks, and is suitable for complex reasoning in fields such as law, finance, and scientific research.
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Creative Writing: The model can generate creative copy, scripts, poetry, etc., meeting needs such as content marketing and advertising copy creation.
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Multilingual Support: Supports tasks in other languages such as English, and has certain multilingual capabilities.
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Multi-Task Capabilities: Performs excellently in tasks such as programming assistance, mathematical problem solving, knowledge Q&A, and multi-turn dialogue, and can generate high-quality code and designs.
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Application Integration: Supports integration into various tools, such as payment software, wealth management assistants, health assistants, etc., improving the level of intelligence.
Technical Principles of Ling-1T
- MoE Architecture: Based on the Mixture of Experts (MoE) architecture, with a total parameter count of 1 trillion, including 256 experts. Each inference activates only about 51 billion parameters, significantly reducing inference costs while maintaining high performance. The first few layers use a dense structure (Dense), then switch to MoE, reducing load imbalance in shallow networks.
- High Reasoning Density Corpus: Pre-trained with over 20T+ tokens of high-quality, high reasoning density corpus to ensure the model has strong logical density and depth of thought. Pre-training is divided into three stages:
- Pretrain Stage 1: 10T high knowledge density corpus.
- Pretrain Stage 2: 10T high reasoning density corpus.
- Mid-training: Extend the context to 128K and add chain-of-thought corpus.
- Efficient Training: Uses FP8 precision training throughout. Compared with BF16, FP8 can significantly save VRAM, improve training speed, and in comparative experiments on 1T tokens, the Loss deviation is only 0.1%.
- LPO Optimization: Uses the Linguistics-Unit Policy Optimization (LPO) method, with sentences as optimization units, which better fits semantic logic and improves the model's reasoning ability and generation quality.
Project Address of Ling-1T
- HuggingFace Model Repository：https://huggingface.co/inclusionAI/Ling-1T
Application Scenarios of Ling-1T
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Programming Assistance: The model can generate high-quality code snippets, helping developers quickly implement functions and improving programming efficiency.
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Mathematical Problem Solving: Performs excellently in mathematical reasoning and problem solving, and supports assisting in solving complex mathematical problems, such as competition questions, etc.
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Knowledge Q&A: The model has strong knowledge understanding ability, can accurately answer various knowledge-based questions, and provides reliable information.
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Creative Writing: The model can generate creative copy, scripts, poetry, etc., meeting the needs of content creation and advertising copy.