README.md

---
license: apache-2.0
---

# InfiR2-7B-base-FP8

<p align="center">
  <a href="https://arxiv.org/abs/2509.22536">📄 Paper</a> &nbsp; | &nbsp;
  <a href="https://github.com/InfiXAI/InfiR2"> 🐙 Github</a> &nbsp; |
  <a href="https://infix-ai.com/research/infir2/">🌐 Project Website</a> &nbsp;
</p>

We performed continual pre-training (CPT) on the **Qwen2.5-7B-base** model for an additional 160 billion tokens using the FP8 format. In this process, both the forward and backward passes employed the E4M3 format, and quantization scaling factors were represented in UE8M0. The training data mixture was composed of:
- 140B tokens from public sources, including FineWeb, Nemotron Datasets and stack-edu and issues-kaggle-notebooks.
- A subsequent 20B tokens mixed with data from AM-DeepSeek-R1 and AM-Qwen3.

The resulting model is the **InfiR2-7B-base-FP8**.


**Training Recipe**:
<p align="center">
    <img src="fp8_recipe.png" width="100%"/>
<p>

- Stable and Reproducible Performance
- Efficient and Low memory Training 



## 🚀 InfiR2 Model Series

The InfiR2 framework offers multiple variants model with different size and training strategy:

- **1.5B**
- [InfiR2-1.5B-base-FP8](https://huggingface.co/InfiX-ai/InfiR2-1.5B-base-FP8): *Continue pretrain on Qwen2.5-1.5B-base*
- [InfiR2-1.5B-Instruct-FP8](https://huggingface.co/InfiX-ai/InfiR2-1.5B-Instruct-FP8): *Supervised fine-tuning on InfiR2-1.5B-base-FP8 with [InfiAlign dataset](https://huggingface.co/papers/2508.05496)*
- **7B**
- [InfiR2-7B-base-FP8](https://huggingface.co/InfiX-ai/InfiR2-7B-base-FP8): *Continue pretrain on Qwen2.5-7B-base*
- [InfiR2-7B-Instruct-FP8](https://huggingface.co/InfiX-ai/InfiR2-7B-Instruct-FP8): *Supervised fine-tuning on InfiR2-7B-base-FP8 with [InfiAlign dataset](https://huggingface.co/papers/2508.05496)*
- [InfiR2-R1-7B-FP8-Preview](https://huggingface.co/InfiX-ai/InfiR2-R1-7B-FP8-Preview): *Multi-stage FP8 Reinforcement Learning*

## 📊 Model Performance
The **InfiR2-7B-Instruct-FP8** model is the result of further fine-tuning applied to the **InfiR2-7B-base-FP8**. For further details, refer to [InfiR2-7B-Instruct-FP8](https://huggingface.co/InfiX-ai/InfiR2-7B-Instruct-FP8). Below is the performance comparison of InfiR2-7B-Instruct-FP8 on reasoning benchmarks. Note: 'w. InfiAlign' denotes Supervised Fine-Tuning (SFT) using the InfiAlign dataset. 

</div>

<div align="center">

<table>
  <thead>
    <tr>
      <th align="left">Model</th>
      <th align="center">AIME 25</th>
      <th align="center">AIME 24</th>
      <th align="center">GPQA</th>
      <th align="center">LiveCodeBench v5</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td align="left"><strong>Deepseek-Distill-Qwen-7B</strong></td>
      <td align="center">43.00</td>
      <td align="center">49.00</td>
      <td align="center">48.20</td>
      <td align="center">37.60</td>
    </tr>
    <tr>
      <td align="left"><strong>Qwen2.5-7B-base (w. InfiAlign)</strong></td>
      <td align="center">33.75</td>
      <td align="center">43.02</td>
      <td align="center">48.11</td>
      <td align="center">39.48</td>
    </tr>
    <tr>
      <td align="left"><strong>InfiR2-7B-Instruct-FP8</strong></td>
      <td align="center">40.62</td>
      <td align="center">55.73</td>
      <td align="center">45.33</td>
      <td align="center">40.31</td>
    </tr>
    </tr>
  </tbody>
</table>

</div>


## 🎭 Quick Start

```python
from vllm import LLM, SamplingParams
import torch
import os

MODEL_NAME = "InfiX-ai/InfiR2-7B-base-FP8"

prompt_text = "Briefly explain what a black hole is, and provide two interesting facts."

MAX_NEW_TOKENS = 256
TEMPERATURE = 0.8
DO_SAMPLE = True

llm = LLM(
    model=MODEL_NAME, 
    dtype="auto", 
)

sampling_params = SamplingParams(
    n=1,
    temperature=TEMPERATURE,
    max_tokens=MAX_NEW_TOKENS,
)

tokenizer = llm.get_tokenizer()
messages = [
    {"role": "user", "content": prompt_text}
]
prompt_formatted = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

outputs = llm.generate(
    prompt_formatted, 
    sampling_params
)

generated_text = outputs[0].outputs[0].text

llm_response = generated_text.strip()

print("\n" + "="*70)
print(f"Prompt: \n{prompt_text}")
print("-" * 70)
print(f"(LLM Response): \n{llm_response}")
print("="*70)
```

## 📚 Model Download

```bash
# Create a directory for models
mkdir -p ./models
# Download InfiR2-7B-base-FP8 model
huggingface-cli download --resume-download InfiX-ai/InfiR2-7B-base-FP8 --local-dir ./models/InfiR2-7B-base-FP8
```
## 🎯 Intended Uses

### ✅ Direct Use

This model is intended for research and commercial use. Example use cases include:

- Mathematical reasoning
- Code generation
- General reasoning

### ❌ Out-of-Scope Use

The model should **not** be used for:

- Generating harmful, offensive, or inappropriate content
- Creating misleading information

## 🙏 Acknowledgements

  * We would like to express our gratitude for the following open-source projects: [Slime](https://github.com/THUDM/slime), [Megatron](https://github.com/NVIDIA/Megatron-LM), [TransformerEngine](https://github.com/NVIDIA/TransformerEngine) and [Qwen2.5](https://github.com/QwenLM/Qwen2.5-Math).

## 📌 Citation

If you find our work useful, please cite:

```bibtex
@misc{wang2025infir2comprehensivefp8training,
      title={InfiR2: A Comprehensive FP8 Training Recipe for Reasoning-Enhanced Language Models}, 
      author={Wenjun Wang and Shuo Cai and Congkai Xie and Mingfa Feng and Yiming Zhang and Zhen Li and Kejing Yang and Ming Li and Jiannong Cao and Hongxia Yang},
      year={2025},
      eprint={2509.22536},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={[https://arxiv.org/abs/2509.22536](https://arxiv.org/abs/2509.22536)}, 
}
```