Platypus: Quick, Cheap, and Powerful Refinement of LLMs (2308.07317v2)

Abstract: We present $\textbf{Platypus}$, a family of fine-tuned and merged LLMs that achieves the strongest performance and currently stands at first place in HuggingFace's Open LLM Leaderboard as of the release date of this work. In this work we describe (1) our curated dataset $\textbf{Open-Platypus}$, that is a subset of other open datasets and which $\textit{we release to the public}$ (2) our process of fine-tuning and merging LoRA modules in order to conserve the strong prior of pretrained LLMs, while bringing specific domain knowledge to the surface (3) our efforts in checking for test data leaks and contamination in the training data, which can inform future research. Specifically, the Platypus family achieves strong performance in quantitative LLM metrics across model sizes, topping the global Open LLM leaderboard while using just a fraction of the fine-tuning data and overall compute that are required for other state-of-the-art fine-tuned LLMs. In particular, a 13B Platypus model can be trained on $\textit{a single}$ A100 GPU using 25k questions in 5 hours. This is a testament of the quality of our Open-Platypus dataset, and opens opportunities for more improvements in the field. Project page: https://platypus-LLM.github.io

• The paper introduces a method that leverages LoRA-based fine-tuning with the curated Open-Platypus dataset to boost LLM performance using minimal resources.
• It employs rigorous data de-duplication and contamination checks alongside model merging strategies to ensure reliable, accurate tuning.
• Numerical results reveal that the 13B model trained on 25K questions in 5 hours surpasses benchmarks, outperforming models like GPT-3.5 and GPT-4 in specific metrics.

Overview of "Platypus: Quick, Cheap, and Powerful Refinement of LLMs"

The paper "Platypus: Quick, Cheap, and Powerful Refinement of LLMs" introduces the Platypus family of LLMs, which have demonstrated superior performance on the HuggingFace Open LLM Leaderboard. The research tackles the challenge of fine-tuning LLMs while minimizing computational resources and data requirements, and importantly, avoids data contamination between training and test sets. The authors present a comprehensive methodology, along with their curated Open-Platypus dataset, which collectively enable robust yet efficient fine-tuning of LLMs.

Key Contributions

The core contributions of the paper are manifold:

• Development and Release of Open-Platypus Dataset: The authors curated a high-quality, small-scale dataset derived from open sources, predominantly featuring STEM and logic-focused content. This dataset facilitates the efficient fine-tuning of LLMs.
• Fine-Tuning Methodology Using LoRA: Utilizing Low-Rank Adaptation (LoRA) modules for fine-tuning models, the process preserves pre-trained model weights while introducing parameter-efficient adjustments, significantly conserving computational costs.
• Data De-duplication and Contamination Check: The authors implemented rigorous processes to remove duplicate content and prevent contamination from overlapping training and test datasets, a crucial step to ensure the validity of fine-tuning efforts.
• Model Merging Strategy: The research explored model merging techniques to combine specialized and general-purpose models, aiming to enhance overall performance across various benchmarks.

Numerical Results

The paper presents strong numerical results, demonstrating that the 13B Platypus model can be trained on a single A100 GPU using 25k questions in just 5 hours. The Platypus2-70B-instruct variant achieved the highest average score on the Hugging Face Open LLM Leaderboard with 73.13%, surpassing both open-source and proprietary models like GPT-3.5 and GPT-4 in certain metrics.

Practical and Theoretical Implications

Practical Implications:

1. Cost-Effective Fine-Tuning: The methodology allows organizations, especially those with limited computational resources, to fine-tune high-performance LLMs economically.
2. Efficient Model Deployment: The approach can facilitate the deployment of state-of-the-art models in real-world applications, such as STEM-related educational tools, scientific research, and more.
3. Enhanced Model Accuracy: By leveraging specialized datasets and combining them with general-purpose models, the Platypus family achieves higher accuracy, thus broadening the scope of potential applications.

Theoretical Implications:

1. Validation of Superficial Alignment Hypothesis: The results resonate with the Superficial Alignment Hypothesis, that extensive model knowledge is gleaned during pre-training, and effective alignment can be achieved with minimal fine-tuning data.
2. Advances in Parameter Efficient Tuning: Through the successful application of LoRA, the research reaffirms the potential of PEFT methods to significantly reduce the computational burden of fine-tuning LLMs.
3. Effectiveness of Model Merging: The practice of merging models with domain-specific fine-tuning reveals interesting insights into the aggregation of diverse knowledge bases within LLMs, indicating a promising scope for further research.

Future Developments

Future research could delve into several avenues:

• Integration of Quantization Techniques: Introducing Quantized-LoRA (QLoRA) into the fine-tuning pipeline could further reduce the computational resource requirements.
• Exploration of Mixture of Experts (MoEs): Investigating the Mixture of Experts approach could optimize the performance of LLMs by leveraging domain-specific tuning more effectively.
• Broadening Dataset Scope: Expanding the Open-Platypus dataset to cover more domains could enhance the versatility of Platypus models, allowing for more tailored applications.
• Enhanced Data Filtering Techniques: Developing more sophisticated methods to detect and remove duplicates or near-duplicates could further ensure the robustness of future models.

Conclusion

The research presented in this paper provides a significant step forward in the efficient fine-tuning of LLMs. The Platypus family leverages a small but potent dataset, advanced fine-tuning techniques, and rigorous data validation methods to achieve top-tier performance with reduced computational demands. These advancements not only facilitate broader access to powerful LLMs but also lay the groundwork for future innovations in model tuning and merging methodologies.