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efa2032

---
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
tags:
- moe
- conversational
library_name: transformers
---

<div align="center">

  <br>
  <h1> InnoMegrez2 </h1>

  <a href="https://github.com/sii-research/InnoMegrez2">
    <b>🔗 Github</b>
  </a> &nbsp;|&nbsp;
  <a href="https://github.com/sii-research/InnoMegrez2/blob/main/docs/tech_report.pdf">
    <b>📄 Tech Report</b>
  </a> &nbsp;|&nbsp;
  <a href="https://huggingface.co/sii-research/InnoMegrez2/blob/main/assets/wechat-official.jpg">
    <b>💬 WeChat Official</b>
  </a> &nbsp;

  <br>

  <strong>[中文](https://huggingface.co/sii-research/InnoMegrez2/blob/main/README_ZH.md) | English</strong>

</div>

## Introduction

InnoMegrez2 is a device native large language model. Megrez2 takes advantages of both the accuracy of Mixture-of-Experts (MoE) architecture and the compact size of Dense models. This release model was trained on 8T Tokens of data. In the future, we plan to improve the model's reasoning and agent capabilities.

## Model Card

<div align="center">

| | |
|:---:|:---:|
| **Architecture** | Mixture-of-Experts (MoE) |
| **Total Parameters** | 3x7B |
| **Activated Parameters** | 3B |
| **Experts Shared Frequency**| 3 |
| **Number of Layers** (Dense layer included) | 31 |
| **Number of Dense Layers** | 1 |
| **Attention Hidden Dimension** | 2048 |
| **MoE Hidden Dimension** (per Expert) | 1408 |
| **Number of Attention Heads** | 16 |
| **Number of Experts** | 64 |
| **Selected Experts per Token** | 6 |
| **Number of Shared Experts** | 4 |
| **Vocabulary Size** | 128,880 |
| **Context Length** | 32K |
| **Base Frequency of RoPE** | 5,000,000 |
| **Attention Mechanism** | GQA |
| **Activation Function** | SwiGLU |
</div>

## Performance

We evaluated InnoMegrez2 using the open-source evaluation tool [OpenCompass](https://github.com/open-compass/opencompass) on several important benchmarks. Some of the evaluation results are shown in the table below.

<div align="center">
<table>
<thead>
<tr>
<th align="center">Benchmark</th>
<th align="center">Metric</th>
<th align="center"><sup>InnoMegrez2<br></sup></th>
<th align="center"><sup>InnoMegrez2<br>-Preview</sup></th>
<th align="center"><sup>SmallThinker-21B<br>-A3B-Instruct</sup></th>
<th align="center"><sup>Qwen3-30B-A3B</sup></th>
<th align="center"><sup>Qwen3-8B</sup></th>
<th align="center"><sup>Qwen3-4B<br>-Instruct-2507</sup></th>
<th align="center"><sup>Phi4-14B<br>(nothink)</sup></th>
<th align="center"><sup>Gemma3-12B</sup></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">Activate Params (B)</td>
<td align="center"></td>
<td align="center">3.0</td>
<td align="center">3.0</td>
<td align="center">3.0</td>
<td align="center">3.3</td>
<td align="center">8.2</td>
<td align="center">4.0</td>
<td align="center">14.7</td>
<td align="center">12.2</td>
</tr>
<tr>
<td align="center">Stored Params (B)</td>
<td align="center"></td>
<td align="center">7.5</td>
<td align="center">7.5</td>
<td align="center">21.5</td>
<td align="center">30.5</td>
<td align="center">8.2</td>
<td align="center">4.0</td>
<td align="center">14.7</td>
<td align="center">12.2</td>
</tr>
<tr>
<td align="center">MMLU</td>
<td align="center">EM</td>
<td align="center">85.4</td>
<td align="center"><strong>87.5</strong></td>
<td align="center">84.4</td>
<td align="center">85.1</td>
<td align="center">81.8</td>
<td align="center">-</td>
<td align="center">84.6</td>
<td align="center">78.5</td>
</tr>
<tr>
<td align="center">GPQA</td>
<td align="center">EM</td>
<td align="center"><strong>58.8</strong></td>
<td align="center">28.8</td>
<td align="center">55.0</td>
<td align="center">44.4</td>
<td align="center">38.9</td>
<td align="center">62</td>
<td align="center">55.5</td>
<td align="center">34.9</td>
</tr>
<tr>
<td align="center">IFEval</td>
<td align="center">Inst<br>loose</td>
<td align="center"><strong>87.7</strong></td>
<td align="center">80.2</td>
<td align="center">85.8</td>
<td align="center">84.3</td>
<td align="center">83.9</td>
<td align="center">83.4</td>
<td align="center">63.2</td>
<td align="center">74.7</td>
</tr>
<tr>
<td align="center">MATH-500</td>
<td align="center">EM</td>
<td align="center"><strong>87.2</strong></td>
<td align="center">81.6</td>
<td align="center">82.4</td>
<td align="center">84.4</td>
<td align="center">81.6</td>
<td align="center">-</td>
<td align="center">80.2</td>
<td align="center">82.4</td>
</tr>
</tbody>
</table>
</div>

## How to Run

### Transformers

The latest version of `transformers` is recommended or `transformers>=4.52.4` is required.
The following contains a code snippet illustrating how to use the model generate content based on given inputs.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

path = "sii-research/InnoMegrez2"
device = "cuda"

tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)

messages = [
    {"role": "user", "content": "世界上最高的山峰是哪座？"},
]
model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(device)

model_outputs = model.generate(
    model_inputs,
    do_sample=True,
    max_new_tokens=1024
)

output_token_ids = [
    model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs))
]

responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
print(responses)

# 世界上最高的山峰是珠穆朗玛峰（Mount Everest），位于喜马拉雅山脉的中尼边境。珠穆朗玛峰的海拔高度为8,848.86米（29,031.7英尺），这一数据是由中国和尼泊尔在2020年共同宣布的最新测量结果。珠穆朗玛峰不仅是登山爱好者的圣地，也是地理和科学研究的重要对象。
```

### ModelScope

`ModelScope` adopts Python API similar to (though not entirely identical to) `Transformers`. For basic usage, simply modify the first line of the above code as follows:

```python
from modelscope import AutoModelForCausalLM, AutoTokenizer
```

## How to Deploy 

InnoMegrez2 support using `vLLM` and `SGLang` as inference backends. For more information, please visit the [gitHub repository](https://github.com/sii-research/InnoMegrez2).

## Best Practice

To achieve optimal performance, we recommend the following settings:

1. Sampling Parameters: we suggest using Temperature=0.7 and TopP=0.9 .
   
2. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.
    * Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
    * Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the answer field with only the choice letter, e.g., "answer": "C"."

## License Agreement

All our open-weight models are licensed under Apache 2.0.

## Contact

If you have any questions, please feel free to submit a GitHub issue or contact [WeChat groups](https://huggingface.co/sii-research/InnoMegrez2/blob/main/assets/wechat-group.jpg).