InfiR2-7B-Instruct-FP8

InfiR2-7B-Instruct-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 supervised fine-tuning on the InfiR2-7B-base-FP8 with FP8 format in two stages using the InfiAlign-SFT-72k and InfiAlign-SFT-165k datasets.

Training Recipe:

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

• Stable and Reproducible Performance
• Efficient and Low memory Training

Hyperparameters:

<div align="center"> </div>

The resulting model is the InfiR2-7B-Instruct-FP8.

🚀 InfiR2 Model Series

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

• 1.5B
• InfiR2-1.5B-base-FP8: Continue pretrain on Qwen2.5-1.5B-base
• InfiR2-1.5B-Instruct-FP8: Supervised fine-tuning on InfiR2-1.5B-base-FP8 with InfiAlign dataset
• 7B
• InfiR2-7B-base-FP8: Continue pretrain on Qwen2.5-7B-base
• InfiR2-7B-Instruct-FP8: Supervised fine-tuning on InfiR2-7B-base-FP8 with InfiAlign dataset
• InfiR2-R1-7B-FP8-Preview: Multi-stage FP8 Reinforcement Learning

📊 Model Performance

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

from vllm import LLM, SamplingParams
import torch
import os

MODEL_NAME = "InfiX-ai/InfiR2-7B-Instruct-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

# Create a directory for models
mkdir -p ./models
# Download InfiR2-7B-Instruct-FP8 model
huggingface-cli download --resume-download InfiX-ai/InfiR2-7B-Instruct-FP8 --local-dir ./models/InfiR2-7B-Instruct-FP8

🎯 Intended Uses

✅ Direct Use

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

• Instruction following
• 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, Megatron, TransformerEngine and Qwen2.5.

📌 Citation

If you find our work useful, please cite:

@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)}, 
}