README.md

---
license: mit
language:
- en
base_model:
- inclusionAI/Ling-flash-base-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- moe
---

# Ring-flash-linear-2.0

<p align="center">
    <img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*4QxcQrBlTiAAAAAAQXAAAAgAemJ7AQ/original" width="100"/>
<p>
<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></p>

## Introduction

We are excited to announce the official open-source release of Ring-flash-linear-2.0!

Building on the success of our Ling 2.0 series, this model continues to leverage a powerful hybrid architecture of linear and standard attention, perfectly balancing high performance with superior efficiency. By integrating our proven MoE design with optimizations like a 1/32 expert activation ratio and MTP layers, Ring-flash-linear achieves the performance of a 40B dense model while activating only 6.1B parameters.  This model was converted from [Ling-flash-base-2.0](https://huggingface.co/inclusionAI/Ling-flash-base-2.0), further trained on an additional 1T tokens.
When it comes to benchmarks, Ring-flash-linear-2.0 not only holds its own against standard attention models (like Ring-flash-2.0) but also outperforms other open-source MoE and Dense models in its class on several demanding tasks. Plus, with support for a 128k long context, it's faster and more precise than ever, especially when handling long-form inputs and outputs.

<div style="display: flex; justify-content: center;">
  <div style="text-align: center;">
    <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/PHRg8ipzJtr0p6sojAa5T.png" width="800">
    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 1:</strong> Hybrid Linear Model Architecture</p>
  </div>
</div>

## Evaluation

To better demonstrate the model's capabilities, we selected representative open-source thinking models and closed-source APIs for comparison. 
We present results on several challenging reasoning benchmarks spanning domains such as mathematics, coding, and science. Also, we evaluate the model's performance on a creative writing task (Creative Writing v3). 
We observe that our model achieves performance on par with other models.

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  <div style="text-align: center;">
    <img src="https://mdn.alipayobjects.com/huamei_t783ie/afts/img/x6YRTIKhf08AAAAAVBAAAAgADgCDAQFr/original" width="1000">
    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 2:</strong> Model Performance Comparison </p>
  </div>
</div>

<div style="display: flex; justify-content: center;">
  <div style="text-align: center;">
    <img src="https://mdn.alipayobjects.com/huamei_t783ie/afts/img/gAbsT5MlNfQAAAAAU6AAAAgADgCDAQFr/original" width="1000">
    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 3:</strong> Model Performance Comparison </p>
  </div>
</div>


## Linear Attention, Highly Sparse, High-Speed Generation

Thanks to its hybrid attention mechanism and highly sparse MoE architecture, Ring-flash-linear-2.0 achieves near-linear time complexity and constant space complexity, resulting in outstanding inference efficiency. 
To fully demonstrate this advantage, we conducted a comparison between our model and top-tier competitors of similar size or performance.
The results clearly demonstrate the advantage of our model in inference efficiency.


<div style="display: flex; justify-content: center; align-items: flex-start; gap: 20px;">
  <div style="text-align: center;">
    <img src="https://mdn.alipayobjects.com/huamei_t783ie/afts/img/wtM_TJ4KVqYAAAAARpAAAAgADgCDAQFr/original" width="500">
    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 4:</strong> Ring-flash-linear-2.0 prefill throughput</p>
  </div>
  
  <div style="text-align: center;">
    <p align="center">
      <img src="https://mdn.alipayobjects.com/huamei_t783ie/afts/img/3n9lSZscvBwAAAAAUhAAAAgADgCDAQFr/original" width="500">
    </p>
    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 5:</strong> Ring-flash-linear-2.0 decode throughput</p>
  </div>

</div>


<!-- ## Model Downloads

<div align="center">

|     **Model**     | **Context Length** | **Download** |
| :----------------: | :----------------: | :----------: |
| Ring-flash-linear-2.0 |        128K         |      [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ring-flash-linear-2.0) <br>[🤖 Modelscope](https://modelscope.cn/models/inclusionAI/Ring-flash-linear-2.0)|
</div> -->

## Quickstart

### Requirements

```bash
pip install flash-linear-attention==0.3.2
pip install transformers==4.56.1
```

### 🤗 Hugging Face Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "inclusionAI/Ring-flash-linear-2.0"

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


prompts = [
    "Give me a short introduction to large language models."
]
input_texts = []
for prompt in prompts:
    messages = [
        {"role": "user", "content": prompt}
    ]
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    input_texts.append(text)

print(input_texts)

model_inputs = tokenizer(input_texts, return_tensors="pt", return_token_type_ids=False, padding=True, padding_side='left').to(model.device)

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

responses = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)

print("*" * 30)
print(responses)
print("*" * 30)
```

### 🚀 SGLang

#### Environment Preparation

We have submitted our [PR](https://github.com/sgl-project/sglang/pull/10917) to SGLang official release and it will be merged later, for now we can prepare the environment following steps, firstly install the community version SGLang and required packages:
```shell
pip install sglang==0.5.2 sgl-kernel==0.3.9.post2 vllm==0.10.2 torch==2.8.0 torchvision==0.23.0 torchao
```

Then you should install our sglang wheel package:
```shell
pip install https://media.githubusercontent.com/media/inclusionAI/Ring-V2/refs/heads/main/hybrid_linear/whls/sglang-0.5.2-py3-none-any.whl --no-deps --force-reinstall
```

#### Run Inference

BF16 and FP8 models are supported by SGLang now, it depends on the dtype of the model in ${MODEL_PATH}. They both share the same command in the following:  

- Start server:
```shell
python -m sglang.launch_server \
    --model-path <model_path> \
    --trust-remote-code \
    --tp-size 4 \
    --disable-radix-cache \
    --tool-call-parser qwen25 \ 
    --json-model-override-args "{\"linear_backend\": \"seg_la\"}"
```

- Client:

```shell
curl -s http://localhost:${PORT}/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "auto", "temperature": 0.6, "messages": [{"role": "user", "content": "Give me a short introduction to large language models."}]}'
```

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

### 🚀 vLLM

#### Environment Preparation

Since the Pull Request (PR) has not been submitted to the vLLM community at this stage, please prepare the environment by following the steps below.

First, create a Conda environment with Python 3.10 and CUDA 12.8:
```shell
conda create -n vllm python=3.10
conda activate vllm
```

Next, install our vLLM wheel package:
```shell
pip install https://media.githubusercontent.com/media/zheyishine/vllm_whl/refs/heads/main/vllm-0.8.5.post2.dev28%2Bgd327eed71.cu128-cp310-cp310-linux_x86_64.whl --force-reinstall
```

Finally, install compatible versions of transformers after vLLM is installed:
```shell
pip install transformers==4.51.1 
```

#### Offline Inference

```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

if __name__ == '__main__':
    tokenizer = AutoTokenizer.from_pretrained("inclusionAI/Ring-flash-linear-2.0", trust_remote_code=True)
    
    sampling_params = SamplingParams(temperature=0.6, top_p=1.0, max_tokens=16384)

    # use `max_num_seqs=1` without concurrency
    llm = LLM(model="inclusionAI/Ring-flash-linear-2.0", dtype='auto', enable_prefix_caching=False, max_num_seqs=128)
    
    
    prompt = "Give me a short introduction to large language models."
    messages = [
        {"role": "user", "content": prompt}
    ]
    
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    outputs = llm.generate([text], sampling_params)
    for output in outputs:
        print(output.outputs[0].text)
```

#### Online Inference
```shell
vllm serve inclusionAI/Ring-flash-linear-2.0 \
              --tensor-parallel-size 2 \
              --pipeline-parallel-size 1 \
              --gpu-memory-utilization 0.90 \
              --max-num-seqs 128 \
              --no-enable-prefix-caching
              --api-key your-api-key
```