# Qwen2.5-32B-Instruct-CFT

## 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).

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