---
license: apache-2.0
datasets:
- TIGER-Lab/WebInstruct-CFT
language:
- en
base_model:
- Qwen/Qwen2.5-32B-Instruct
tags:
- cft
- math
- reasoning
pipeline_tag: text-generation
library_name: transformers
---

# Qwen2.5-32B-Instruct-CFT

<div style="display: flex; gap: 4px; align-items: center">
  <a target="_blank" href="https://github.com/TIGER-AI-Lab/CritiqueFinetuning">
    <img style="height:18pt" src="https://img.shields.io/badge/-Code-black?style=flat&logo=github"/>
  </a>
  <a target="_blank" href="https://arxiv.org/abs/2501.17703">
    <img style="height:18pt" src="https://img.shields.io/badge/-Paper-green?style=flat&logo=arxiv"/>
  </a>
  <a target="_blank" href="https://tiger-ai-lab.github.io/CritiqueFineTuning">
    <img style="height:18pt" src="https://img.shields.io/badge/-📖%20Website-red?style=flat"/>
  </a>
  <a target="_blank" href="https://huggingface.co/datasets/TIGER-Lab/WebInstruct-CFT">
    <img style="height:18pt" src="https://img.shields.io/badge/-🤗%20Dataset-red?style=flat"/>
  </a>
</div>

## Introduction

Qwen2.5-32B-Instruct-CFT is a 32B parameter model fine-tuned using our novel Critique Fine-Tuning (CFT) approach. Built upon the Qwen2.5-32B-Instruct base model, this variant is trained to critique and analyze responses rather than simply imitate them, leading to enhanced reasoning capabilities.

## Key Features

- Built on the powerful Qwen2.5-32B-Instruct foundation
- Trained using Critique Fine-Tuning (CFT) methodology
- Highly efficient training with minimal data requirements
- Inherits the strong instruction-following capabilities of the base model

## Training Details

### Training Data
- Dataset: [WebInstruct-CFT-4K](https://huggingface.co/datasets/TIGER-Lab/WebInstruct-CFT-4K)
- Training format: (input=[query; noisy response], output=critique)
- Teacher model: GPT-4o for generating critiques

### Training Infrastructure
- Framework: LLaMA-Factory
- Hardware: 8x NVIDIA H100 GPUs
- Training time: ~1.5 hours with DeepSpeed Zero-3

For more details about the model architecture, methodology, and comprehensive evaluation results, please visit our [project webpage](https://tiger-ai-lab.github.io/CritiqueFineTuning).