The text discusses methods to boost the performance of fine-tuned models, particularly Large Language Models (LLMs) using Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO). It details the formatting of preference datasets, training the model with DPO, and evaluating the performance of the model. The process results in the creation of a new model, NeuralHermes-2.5, which shows significant improvement on the Open LLM Leaderboard.
Boost Performance with Direct Preference Optimization
Boost the performance of your supervised fine-tuned models with Direct Preference Optimization (DPO), a practical AI solution that improves the behavior of pre-trained Large Language Models (LLMs). Created NeuralHermes-2.5 by fine-tuning OpenHermes-2.5 using a DPO-like technique. In this article, we’ll explain how DPO significantly enhances model performance based on real-world application.
Preference Datasets
Preference datasets are collections of ranked answers by humans. These rankings guide the fine-tuning of LLMs to output preferred answers. However, creating these datasets can be costly and prone to biases. To address these issues, several solutions, like replacing human feedback with AI feedback, are available. Despite being smaller than fine-tuning datasets, preference datasets play a crucial role in improving LLM performance.
Direct Preference Optimization
Direct Preference Optimization (DPO) simplifies the control process by treating the task as a classification problem. By leveraging the LLM itself as a reward model, DPO efficiently aligns the model’s outputs with human preferences, resulting in a more stable, efficient, and computationally less demanding process compared to traditional methods.
Formatting the Data
We demonstrated how to fine-tune the OpenHermes-2.5-Mistral-7B model using the Intel/orca_dpo_pairs dataset. The dataset was formatted using a specific chat template, and the process was streamlined using the tokenizer’s apply_chat_template() function.
Training the Model with DPO
We defined LoRA configurations and loaded the model for fine-tuning with DPO. The training process, including fine-tuning the model and evaluating its performance, was explained step by step. The model’s performance was evaluated, and the significant improvement in the average score compared to the original model was highlighted.
Conclusion
We showcased the practical application of DPO in fine-tuning LLMs and creating our own model, NeuralHermes-2.5. The article emphasized the potential for improvement in the fine-tuning pipeline and provided references for further learning.
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