Summary of Unveiling the Secret Recipe: a Guide For Supervised Fine-tuning Small Llms, by Aldo Pareja et al.
Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs
by Aldo Pareja, Nikhil Shivakumar Nayak, Hao Wang, Krishnateja Killamsetty, Shivchander Sudalairaj, Wenlong Zhao, Seungwook Han, Abhishek Bhandwaldar, Guangxuan Xu, Kai Xu, Ligong Han, Luke Inglis, Akash Srivastava
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive study on supervised fine-tuning of large language models (LLMs) using instruction-tuning datasets spanning diverse knowledge domains and skills. The authors aim to bridge the gap between industrial research labs and individual developers by exploring various training configurations and strategies for small-sized LLMs (3B to 7B parameters). They provide detailed documentation of these configurations, revealing findings that challenge common training practices. The study finds that larger batch sizes paired with lower learning rates lead to improved model performance on benchmarks such as MMLU, MTBench, and Open LLM Leaderboard. Early-stage training dynamics are strong indicators of better final model performance, enabling early termination of sub-optimal runs and significant computational savings. The authors also provide guidance for practitioners by exploring hyperparameters like warmup steps and learning rate schedules. Additionally, the study shows that there is no significant difference in performance between phased and stacked training strategies, but stacked training is simpler and more sample efficient. With these findings holding robustly across datasets and models, the paper aims to serve as a guide for practitioners fine-tuning small LLMs and promote a more inclusive environment for LLM research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how to improve language models by “fine-tuning” them with new data. The authors test different ways of doing this on four pre-trained models, and find that certain techniques work better than others. They also explore why these techniques work, which helps us understand what makes a good language model. The main findings are that using bigger batches and lower learning rates can improve performance, and that early signs of good or bad performance can help stop training runs early to save time. The authors also provide guidance for people who want to use their own data to fine-tune models. Overall, the paper aims to make language models more accessible to everyone, not just big research labs with lots of resources. |
Keywords
» Artificial intelligence » Fine tuning » Instruction tuning » Language model » Supervised
