Qwen2.5-32B-Instruct-CFT

Maintained By
TIGER-Lab

Qwen2.5-32B-Instruct-CFT

PropertyValue
Parameter Count32 Billion
Model TypeInstruction-tuned LLM
Training FrameworkLLaMA-Factory
Model URLHuggingFace

What is Qwen2.5-32B-Instruct-CFT?

Qwen2.5-32B-Instruct-CFT represents a significant advancement in language model development, built upon the foundation of Qwen2.5-32B-Instruct. What sets this model apart is its innovative Critique Fine-Tuning (CFT) approach, which trains the model to analyze and critique responses rather than simply generating them. This methodology enhances the model's reasoning capabilities while maintaining the strong instruction-following abilities of its base model.

Implementation Details

The model was trained using the WebInstruct-CFT-4K dataset, with GPT-4 serving as the teacher model for generating critiques. The training infrastructure utilized 8 NVIDIA H100 GPUs and implemented DeepSpeed Zero-3 for efficient processing, completing the training in approximately 1.5 hours.

  • Employs Critique Fine-Tuning methodology for enhanced analytical capabilities
  • Built on the powerful Qwen2.5-32B-Instruct foundation
  • Highly efficient training process with minimal data requirements
  • Utilizes advanced training format: (input=[query; noisy response], output=critique)

Core Capabilities

  • Advanced reasoning and analytical abilities
  • Strong instruction-following capabilities inherited from base model
  • Efficient critique generation
  • Enhanced response evaluation

Frequently Asked Questions

Q: What makes this model unique?

The model's distinctive feature is its Critique Fine-Tuning approach, which enables it to not just generate responses but also analyze and critique them, leading to more robust and reasoned outputs.

Q: What are the recommended use cases?

This model is particularly well-suited for applications requiring critical analysis, response evaluation, and complex reasoning tasks. It can be valuable in educational contexts, content analysis, and automated review systems.

🍰 Interesting in building your own agents?
PromptLayer provides Huggingface integration tools to manage and monitor prompts with your whole team. Get started here.