README.md

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

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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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### 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](https://zenmux.ai/inclusionai/ling-1t?utm_source=hf_inclusionAI) 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](https://zenmux.ai/inclusionai/ling-1t?utm_source=hf_inclusionAI) 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（[arXiv:2507.17634](https://arxiv.org/abs/2507.17634)） 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.

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## 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

#### 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.

For detailed guidance, please refer to the vLLM [`instructions`](https://docs.vllm.ai/en/latest/).



## Limitations & Future Plans

While **[Ling-1T](https://zenmux.ai/inclusionai/ling-1t?utm_source=hf_inclusionAI)** 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**