---
license: mit
pipeline_tag: text-generation
library_name: transformers
---

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    <img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*4QxcQrBlTiAAAAAAQXAAAAgAemJ7AQ/original" width="100"/>
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<p align="center">🤗 <a href="https://huggingface.co/inclusionAI">Hugging Face</a>&nbsp;&nbsp; | &nbsp;&nbsp;🤖 <a href="https://modelscope.cn/organization/inclusionAI">ModelScope </a>&nbsp;&nbsp; | &nbsp;&nbsp;🐙 <a href="https://zenmux.ai/inclusionai/ling-1t?utm_source=hf_inclusionAI">Experience Now</a></p>


## Introduction

**Ling-1T** is the first flagship *non-thinking* model in the Ling 2.0 series, featuring **1 trillion total parameters** with **≈ 50 billion active parameters per token**.
Built on the Ling 2.0 architecture, Ling-1T is designed to push the limits of *efficient reasoning* and *scalable cognition*.

Pre-trained on **20 trillion+ high-quality, reasoning-dense tokens**, Ling-1T-base supports up to **128K context length** and adopts an **evolutionary chain-of-thought (Evo-CoT)** process across mid-training and post-training.
This curriculum greatly enhances the model’s efficiency and reasoning depth, allowing Ling-1T to achieve **state-of-the-art performance** on multiple complex reasoning benchmarks—balancing **accuracy** and **efficiency**.


### Flagship-Level Efficient Reasoning

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We comprehensively evaluated Ling-1T against leading flagship models, including both **open-source giants** (e.g., *DeepSeek-V3.1-Terminus*, *Kimi-K2-Instruct-0905*) and **closed-source APIs** (*GPT-5-main*, *Gemini-2.5-Pro*).
Across code generation, software development, competition-level mathematics, professional math, and logical reasoning, Ling-1T consistently demonstrates **superior complex reasoning ability** and overall advantage.

In the **AIME 25** benchmark, Ling-1T extends the **Pareto frontier** of reasoning accuracy vs. reasoning length, showcasing its strength in **“efficient thinking and precise reasoning.”**

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    <img src="https://mdn.alipayobjects.com/huamei_bcz3yt/afts/img/J8ciS5KbIrwAAAAAceAAAAgADkV7AQFr/original"/>
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### Aesthetic Understanding and Front-End Generation

Ling-1T excels in visual reasoning and front-end code generation tasks, combining deep semantic understanding with precise code synthesis.
We introduce a hybrid *Syntax–Function–Aesthetics* reward mechanism, enabling the model to not only generate correct and functional code but also demonstrate a refined sense of **visual aesthetics**.
On **ArtifactsBench**, Ling-1T ranks **first among open-source models**, and the benchmark visualizations in this card were, in fact, *generated by Ling-1T itself*.


### Emergent Intelligence at Trillion-Scale

Scaling to the trillion-parameter level has revealed strong **emergent reasoning and transfer capabilities**.
For example, in the **BFCL V3** tool-use benchmark, Ling-1T achieves **≈ 70% tool-call accuracy** with only light instruction tuning—despite having seen no large-scale trajectory data during training.
Ling-1T can:

* Interpret complex natural-language instructions
* Transform abstract logic into functional visual components
* Generate cross-platform compatible front-end code
* Create stylistically controlled marketing copy and multi-lingual text

These capabilities form the foundation for **general, collaborative human–AI intelligence**, which we aim to advance together with the open-source community through Ling-1T’s release.


### Pre-Training at Trillion Scale

The Ling 2.0 architecture was designed from the ground up for trillion-scale efficiency, guided by the **Ling Scaling Law** ([arXiv:2507.17702](https://arxiv.org/abs/2507.17702)).
This ensures architectural and hyperparameter scalability even under **1e25–1e26 FLOPs** of compute.

Key architectural innovations include:

* **1T total / 50B active parameters** with a **1/32 MoE activation ratio**
* **MTP layers** for enhanced compositional reasoning
* **Aux-loss-free**, **sigmoid-scoring expert routing** with **zero-mean updates**
* **QK Normalization** for fully stable convergence

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Ling-1T is the **largest FP8-trained foundation model** known to date.
FP8 mixed-precision training yields **15%+ end-to-end speedup**, improved memory efficiency, and maintains **≤ 0.1% loss deviation** from BF16 across **1T tokens**.
A fine-grained, **heterogeneous 1F1B interleaved pipeline** further boosts utilization by 40 %+.
System-level optimizations—fused kernels, communication scheduling, recomputation, checkpointing, simulation, and telemetry—ensure stable trillion-scale training.

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Pre-training used over **20T high-quality tokens**, with **> 40% reasoning-dense data** in later stages.
Mid-training introduced **curated chain-of-thought corpora** for “**reasoning pre-activation**”, improving downstream reasoning stability.
A custom **WSM (Warmup–Stable–Merge)** LR scheduler with mid-train checkpoint merging simulates LR decay and boosts generalization.


### Post-Training and Evo-CoT Optimization

Built upon mid-training reasoning activation, post-training adopts **Evo-CoT (Evolutionary Chain-of-Thought)** for progressive reasoning enhancement under controllable cost.
This approach continually expands the **Pareto frontier** of reasoning accuracy vs. efficiency—ideal for reflexive non-thinking models.

For reinforcement learning, we introduce **LPO (Linguistics-Unit Policy Optimization)** —a novel sentence-level policy optimization method.
Unlike GRPO (token-level) or GSPO (sequence-level) algorithms, LPO treats *sentences* as the natural semantic action units, enabling precise alignment between rewards and reasoning behavior.
Empirically, LPO offers superior **training stability** and **generalization** across reasoning tasks.

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## Evaluation

Ling-1T has been extensively evaluated across **knowledge**, **code**, **math**, **reasoning**, **agent**, and **alignment** benchmarks.
It currently stands as the **best open-source flagship non-thinking model**, rivaling closed-source APIs in complex reasoning while maintaining exceptional efficiency and interpretability.

## Evaluation
| Task                  | Benchmark                  | DeepSeek-V3.1-Terminus                   | Kimi-K2-Instruct-0905                    | gpt-5-main | Gemini 2.5 Pro                           | Ling-1T                                  |
| --------------------- | -------------------------- | ---------------------------------------- | ---------------------------------------- | ---------- | ---------------------------------------- | ---------------------------------------- |
|                       |                            | (NonThinking)                            |                                          |            | (thinkBudget=128)                        |                                          |
| **Knowledge**         | **Professional Knowledge** |                                          |                                          |            |                                          |                                          |
|                       | C-Eval                     | __91.76__                                | 91.12                                    | 83.59      | 88.77                                    | __<span style="color:red">92.19</span>__ |
|                       | MMLU-Redux (EM)            | 92.37                                    | 91.58                                    | **92.75**  | __<span style="color:red">94.67</span>__ | 92.25                                    |
|                       | MMLU-Pro                   | __<span style="color:red">83.25</span>__ | 81.03                                    | 81.94      | **82.13**                                | 82.04                                    |
| **Knowledge**         | **STEM**                   |                                          |                                          |            |                                          |                                          |
|                       | MMLU-Pro-Stem              | 87.91                                    | 85.30                                    | 73.45      | __<span style="color:red">88.60</span>__ | **88.5**                                 |
|                       | OlympiadBench-stem         | 87.83                                    | 79.13                                    | 78.26      | **89.57**                                | __<span style="color:red">91.3</span>__  |
|                       | GPQA-Diamond               | __<span style="color:red">76.23</span>__ | **73.93**                                | 71.31      | 71.81                                    | 72.98                                    |
| **Coding**            | **Code Generation**        |                                          |                                          |            |                                          |                                          |
|                       | MultiPL-E                  | **77.68**                                | 73.76                                    | 76.66      | 71.48                                    | __<span style="color:red">77.91</span>__ |
|                       | mbpp                       | 90.69                                    | 89.96                                    | **91.72**  | 91.01                                    | __<span style="color:red">96.87</span>__ |
|                       | LiveCodeBench (2408-2505)  | 48.02                                    | 48.95                                    | **48.57**  | 45.43                                    | __<span style="color:red">61.68</span>__ |
|                       | CodeForces-rating          | 1582                                     | 1574                                     | 1120       | **1675**                                 | __<span style="color:red">1901</span>__  |
|                       | BIRD_SQL                   | 44.88                                    | 46.45                                    | 43.97      | __<span style="color:red">54.76</span>__ | **52.38**                                |
| **Coding**            | **Software Development**   |                                          |                                          |            |                                          |                                          |
|                       | ArtifactsBench             | 43.29                                    | 44.87                                    | 41.04      | __<span style="color:red">60.28</span>__ | **59.31**                                |
|                       | FullStack Bench            | **55.48**                                | 54.00                                    | 50.92      | 48.19                                    | __<span style="color:red">56.55</span>__ |
|                       | Aider                      | **88.16**                                | 85.34                                    | 84.40      | __<span style="color:red">89.85</span>__ | 83.65                                    |
| **Math**              | **Competition Math**       |                                          |                                          |            |                                          |                                          |
|                       | CNMO 2024                  | 73.78                                    | 68.92                                    | 63.11      | **74.65**                                | __<span style="color:red">79.25</span>__ |
|                       | AIME 2025                  | 55.21                                    | 50.16                                    | 59.43      | **70.10**                                | __<span style="color:red">70.42</span>__ |
|                       | UGMathBench                | **72.70**                                | 69.97                                    | 67.27      | 70.10                                    | __<span style="color:red">74.95</span>__ |
|                       | Omni-Math                  | 64.77                                    | 62.42                                    | 61.09      | **72.02**                                | __<span style="color:red">74.46</span>__ |
| **Math**              | **Professional Math**      |                                          |                                          |            |                                          |                                          |
|                       | FinanceReasoning           | 86.44                                    | 84.83                                    | 86.28      | **86.65**                                | __<span style="color:red">87.45</span>__ |
|                       | Optibench                  | 64.30                                    | 60.83                                    | 40.06      | **68.76**                                | __<span style="color:red">74.71</span>__ |
|                       | OptMATH                    | 35.99                                    | 35.84                                    | 39.16      | **42.77**                                | __<span style="color:red">57.68</span>__ |
| **General Reasoning** |                            |                                          |                                          |            |                                          |                                          |
|                       | BBEH                       | **42.86**                                | 34.83                                    | 39.75      | 29.08                                    | __<span style="color:red">47.34</span>__ |
|                       | KOR-Bench                  | **73.76**                                | 73.20                                    | 70.56      | 59.68                                    | __<span style="color:red">76.00</span>__ |
|                       | ARC-AGI-1                  | 14.69                                    | **22.19**                                | 14.06      | 18.94                                    | __<span style="color:red">43.81</span>__ |
|                       | ZebraLogic                 | 81.6                                     | **85.5**                                 | 57.3       | 70.2                                     | __<span style="color:red">90.8</span>__  |
| **Agent**             |                            |                                          |                                          |            |                                          |                                          |
|                       | BFCL-V3                    | 52.67                                    | __<span style="color:red">71.05</span>__ | 50.27      | 63.31                                    | **69.64**                                |
| **Alignment**         |                            |                                          |                                          |            |                                          |                                          |
|                       | Arena Hard V2 ELO          | 54.09                                    | __<span style="color:red">76.95</span>__ | 68.37      | 65.37                                    | **76.26**                                |
|                       | Arena Hard V2 Win Rate     | 63.24                                    | 69.88                                    | 65.06      | **74.46**                                | __<span style="color:red">75.83</span>__ |
|                       | writing_bench              | 80.95                                    | **87.59**                                | 77.07      | 80.53                                    | __<span style="color:red">89.4</span>__  |
|                       | Creative Writing v3        | 85.18                                    | **87.01**                                | 80.93      | 84.99                                    | <span style="color:red">89.24</span>     |
|                       | MultiChallenge             | 42.49                                    | 48.72                                    | 48.72      | **51.28**                                | __<span style="color:red">58.24</span>__ |



## Model Downloads

You can download Ling-1T from the following table. If you are located in mainland China, we also provide the model on ModelScope.cn to speed up the download process.

<center>

| **Model** | **Context Length** |                                                                 **Download**                                                                  |
| :-------: | :----------------: | :-------------------------------------------------------------------------------------------------------------------------------------------: |
|  Ling-1T  | 32K -> 128K (YaRN) | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-1T) &nbsp;&nbsp; [🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-1T) |

</center>

Note: If you are interested in previous version, please visit the past model collections in [Huggingface](https://huggingface.co/inclusionAI) or [ModelScope](https://modelscope.cn/organization/inclusionAI).


## Quickstart

### 🚀 Try Online

You can experience Ling-1T online at: [ZenMux](https://zenmux.ai/inclusionai/ling-1t?utm_source=hf_inclusionAI)

### 🔌 API Usage

You can also use Ling-1T through API calls:

```python   
from openai import OpenAI

# 1. Initialize the OpenAI client
client = OpenAI(
    # 2. Point the base URL to the ZenMux endpoint
    base_url="https://zenmux.ai/api/v1",
    # 3. Replace with the API Key from your ZenMux user console
    api_key="<your ZENMUX_API_KEY>",
)

# 4. Make a request
completion = client.chat.completions.create(
    # 5. Specify the model to use in the format "provider/model-name"
    model="inclusionai/ling-1t",
    messages=[
        {
            "role": "user",
            "content": "What is the meaning of life?"
        }
    ]
)

print(completion.choices[0].message.content)
```

## Deployment

### SGLang

#### Environment Preparation

We will later submit our model to the SGLang official release. Now we can prepare the environment by following these steps:
```shell
pip3 install -U sglang sgl-kernel
```

#### Run Inference

Both BF16 and FP8 models are supported by SGLang now. It depends on the dtype of the model in ${MODEL_PATH}.

Here is the example to run Ling-1T with multiple GPU nodes, where the master node IP is ${MASTER_IP} and server port is ${PORT}:

- Start server:
```bash
# Node 0:
python -m sglang.launch_server --model-path $MODEL_PATH --tp-size 8 --pp-size 4 --dp-size 1 --trust-remote-code --dist-init-addr $MASTER_IP:2345 --port $PORT --nnodes 4 --node-rank 0 

# Node 1:
python -m sglang.launch_server --model-path $MODEL_PATH --tp-size 8 --pp-size 4 --dp-size 1 --trust-remote-code --dist-init-addr $MASTER_IP:2345 --port $PORT --nnodes 4 --node-rank 1 

# Node 2:
python -m sglang.launch_server --model-path $MODEL_PATH --tp-size 8 --pp-size 4 --dp-size 1 --trust-remote-code --dist-init-addr $MASTER_IP:2345 --port $PORT --nnodes 4 --node-rank 2 

# Node 3:
python -m sglang.launch_server --model-path $MODEL_PATH --tp-size 8 --pp-size 4 --dp-size 1 --trust-remote-code --dist-init-addr $MASTER_IP:2345 --port $PORT --nnodes 4 --node-rank 3

# This is only an example. Please adjust arguments according to your actual environment.
```

- Client:

```shell
curl -s http://${MASTER_IP}:${PORT}/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "auto", "messages": [{"role": "user", "content": "What is the capital of France?"}]}'
```

More usage can be found [here](https://docs.sglang.ai/basic_usage/send_request.html)

### vLLM

For latest guidance, please refer to the vLLM [`instructions`](https://docs.vllm.ai/projects/recipes/en/latest/inclusionAI/Ring-1T-FP8.html).

#### Environment Preparation

```bash
pip install vllm==0.11.0
```

#### Run Inference:

Here is the example to deploy the model with multiple GPU nodes, where the master node IP is ${MASTER_IP}, server port is ${PORT} and the path of model is ${MODEL_PATH}:

```bash
# step 1. start ray on all nodes

# step 2. start vllm server only on node 0:
vllm serve $MODEL_PATH --port $PORT --served-model-name my_model --trust-remote-code --tensor-parallel-size 32 --gpu-memory-utilization 0.85

# This is only an example, please adjust arguments according to your actual environment.
```

To handle long context in vLLM using YaRN, we need to follow these two steps:
1. Add a `rope_scaling` field to the model's `config.json` file, for example:
```json
{
  ...,
  "rope_scaling": {
    "factor": 4.0,
    "original_max_position_embeddings": 32768,
    "type": "yarn"
  }
}
```
2. Use an additional parameter `--max-model-len` to specify the desired maximum context length when starting the vLLM service.


## Limitations & Future Plans

While **Ling-1T** has made strong progress in efficient reasoning, cross-domain generalization, and training efficiency, several limitations remain:

* **GQA-based attention**: stable for long-context reasoning but relatively costly. Future versions will adopt **hybrid attention** to improve efficiency.
* **Limited agentic ability**: current model has room to grow in multi-turn interaction, long-term memory, and tool use.
* **Instruction and identity issues**: occasional deviations or role confusion may occur; future updates will enhance **alignment and consistency**.

The future versions of Ling-1T will continue to evolve in architecture, reasoning, and alignment, advancing the series toward more general intelligence.


## License

This code repository is licensed under [the MIT License](https://github.com/inclusionAI/Ling-V2/blob/main/LICENSE).

## FAQ

Recommended temperature? **0.7**  
Recommended top_p? **0.95** 

## Citation

If you find our work helpful, feel free to give our paper a citation.

```bibtex
@article{Ling-Team2025,
  author = {Ling-Team and 141 others},
  title = {{Every Activation Boosted: Scaling General Reasoner to 1 Trillion Open Language Foundation}},
  journal = {arXiv preprint arXiv:2510.22115},
  eprint = {2510.22115},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  year = {2025},
  doi = {10.48550/arXiv.2510.22115},
  url = {https://arxiv.org/abs/2510.22115}
}