Papers by Yaowei Zheng
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models (2024.acl-demos)
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| Challenge: | Efficient fine-tuning of large language models requires non-trivial efforts to implement these methods on different models. |
| Approach: | They propose a framework that democratizes the fine-tuning of large language models by integrating a suite of efficient training methods into one framework. |
| Outcome: | The proposed framework is able to scale to 100+ LLMs without coding and receives over 25,000 stars and 3,000 forks. |
Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents (2025.emnlp-demos)
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| Challenge: | Existing data synthesis tools struggle to extract reliable fine-tuning data from heterogeneous documents. |
| Approach: | They propose a framework for synthesizing fine-tuning data from unstructured documents via an intuitive graphical user interface. |
| Outcome: | The proposed framework can extract reliable data from unstructured documents via an intuitive graphical user interface (GUI) it leverages persona-driven prompting approach to generate diverse question-answer pairs using public-available LLMs. |