Kyleyee/ORPO_hh-seed4
Kyleyee/ORPO_hh-seed4 is a 1.5 billion parameter language model fine-tuned by Kyleyee, based on the Qwen2.5-1.5B-sft-hh-3e architecture. It was trained using the ORPO method on the Helpful_drdpo_preference dataset, specializing in generating helpful and preference-aligned responses. This model is designed for conversational AI applications requiring nuanced and contextually appropriate text generation within a 32768 token context length.
Loading preview...
Model Overview
Kyleyee/ORPO_hh-seed4 is a 1.5 billion parameter language model developed by Kyleyee. It is a fine-tuned variant of the Qwen2.5-1.5B-sft-hh-3e base model, specifically optimized for generating helpful and preference-aligned text.
Training Methodology
This model was trained using the ORPO (Monolithic Preference Optimization without Reference Model) method, as detailed in the paper "ORPO: Monolithic Preference Optimization without Reference Model" (arXiv:2403.07691). The training utilized the Kyleyee/train_data_Helpful_drdpo_preference dataset and the TRL framework.
Key Features
- Preference Optimization: Leverages the ORPO method for direct preference alignment, aiming to produce more helpful and desired outputs.
- Base Model: Built upon the Qwen2.5-1.5B architecture, providing a solid foundation for language understanding and generation.
- Context Length: Supports a substantial context window of 32768 tokens, enabling processing of longer inputs and generating extended responses.
Intended Use Cases
This model is particularly suitable for applications where generating helpful, aligned, and contextually relevant text is crucial, such as:
- Conversational AI: Enhancing chatbots and virtual assistants to provide more useful and preferred responses.
- Content Generation: Creating text that adheres to specific helpfulness criteria.
- Preference-aligned tasks: Any task requiring a model to generate outputs based on learned human preferences.