README.md

---
license: mit
base_model:
  - inclusionAI/Ling-mini-base-2.0-20T
pipeline_tag: text-generation
library_name: transformers
---

# Ring-mini-2.0

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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/ring-mini-2.0">ChatNow</a></p>

Today, we officially release Ring-mini-2.0 — a high-performance inference-oriented MoE model deeply optimized based on the Ling 2.0 architecture. With only 16B total parameters and 1.4B activated parameters, it achieves comprehensive reasoning capabilities comparable to dense models below the 10B scale. It excels particularly in logical reasoning, code generation, and mathematical tasks, while supporting 128K long-context processing and 300+ tokens/s high-speed generation.

## Enhanced Reasoning: Joint Training with SFT + RLVR + RLHF

Built upon Ling-mini-2.0-base, Ring-mini-2.0 undergoes further training with Long-CoT SFT, more stable and continuous RLVR, and RLHF joint optimization, significantly improving the stability and generalization of complex reasoning. On multiple challenging benchmarks (LiveCodeBench, AIME 2025, GPQA, ARC-AGI-v1, etc.), it outperforms dense models below 10B and even rivals larger MoE models (e.g., gpt-oss-20B-medium) with comparable output lengths, particularly excelling in logical reasoning.

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## High Sparsity, High-Speed Generation

Inheriting the efficient MoE design of the Ling 2.0 series, Ring-mini-2.0 activates only 1.4B parameters and achieves performance equivalent to 7–8B dense models through architectural optimizations such as 1/32 expert activation ratio and MTP layers. Thanks to its low activation and high sparsity design, Ring-mini-2.0 delivers a throughput of 300+ tokens/s when deployed on H20. With Expert Dual Streaming inference optimization, this can be further boosted to 500+ tokens/s, significantly reducing inference costs for high-concurrency scenarios involving thinking models. Additionally, with YaRN extrapolation, it supports 128K long-context processing, achieving a relative speedup of up to 7x in long-output scenarios.

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    <img src="https://mdn.alipayobjects.com/huamei_d2byvp/afts/img/gjJKSpFVphEAAAAAgdAAAAgADod9AQFr/original" width="1000"/>
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    <img src="https://mdn.alipayobjects.com/huamei_d2byvp/afts/img/o-vGQadCF_4AAAAAgLAAAAgADod9AQFr/original" width="1000"/>
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## Model Downloads

<div align="center">

|   **Model**   | **#Total Params** | **#Activated Params** | **Context Length** |                                                                  **Download**                                                                  |
| :-----------: | :---------------: | :-------------------: | :----------------: | :--------------------------------------------------------------------------------------------------------------------------------------------: |
| Ring-mini-2.0 |       16.8B       |         1.4B          |        128K        | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ring-mini-2.0) <br>[🤖 Modelscope](https://modelscope.cn/models/inclusionAI/Ring-mini-2.0) |

</div>

## Quickstart

### 🚀 Try Online

You can experience Ring-mini-2.0 online at: [ZenMux](https://zenmux.ai/inclusionai/ring-mini-2.0)

### 🔌 API Usage

You can also use Ring-mini-2.0 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/Ring-mini-2.0",
    messages=[
        {
            "role": "user",
            "content": "What is the meaning of life?"
        }
    ]
)

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

### 🤗 Hugging Face Transformers

Here is a code snippet to show you how to use the chat model with `transformers`:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "inclusionAI/Ring-mini-2.0"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Give me a short introduction to large language models."
messages = [
    {"role": "system", "content": "You are Ring, an assistant created by inclusionAI"},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt", return_token_type_ids=False).to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=8192
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```

## License

This code repository is licensed under [the MIT License](https://huggingface.co/inclusionAI/Ring-mini-2.0/blob/main/LICENSE).

## Citation

TODO