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Llamafactory: Unified Efficient Fine-tuning Of 100+ Language Models

Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo . No Venue 2024

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Compositional Generalization Content Enrichment Efficiency Fine Tuning Has Code Interdisciplinary Approaches Multimodal Semantic Representation Productivity Enhancement RAG Tools Training Techniques Variational Autoencoders

Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks. However, it requires non-trivial efforts to implement these methods on different models. We present LlamaFactory, a unified framework that integrates a suite of cutting-edge efficient training methods. It allows users to flexibly customize the fine-tuning of 100+ LLMs without the need for coding through the built-in web UI LlamaBoard. We empirically validate the efficiency and effectiveness of our framework on language modeling and text generation tasks. It has been released at https://github.com/hiyouga/LLaMA-Factory and already received over 13,000 stars and 1,600 forks.

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