Kyleyee/DrDPO_hh-seed3
Kyleyee/DrDPO_hh-seed3 is a 1.5 billion parameter language model fine-tuned by Kyleyee using Direct Preference Optimization (DPO). Based on the Qwen2.5-1.5B-sft-hh-3e architecture, this model is optimized for helpfulness, leveraging the Kyleyee/train_data_Helpful_drdpo_preference dataset. It features a 32768 token context length and is designed for generating helpful and aligned text responses.
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Overview
Kyleyee/DrDPO_hh-seed3 is a 1.5 billion parameter language model developed by Kyleyee. It is a fine-tuned version of the Qwen2.5-1.5B-sft-hh-3e model, specifically optimized for generating helpful responses. The model was trained using Direct Preference Optimization (DPO), a method that aligns language models with human preferences by treating the preference data as implicit reward signals.
Key Capabilities
- Helpful Response Generation: Fine-tuned on a dataset specifically designed for helpfulness, making it suitable for tasks requiring informative and useful outputs.
- Preference Alignment: Utilizes the DPO training method to align its outputs with desired human preferences, enhancing the quality and relevance of generated text.
- Extended Context Window: Supports a context length of 32768 tokens, allowing it to process and generate longer, more coherent texts while maintaining context.
Training Details
The model's training procedure involved using the TRL library and the Kyleyee/train_data_Helpful_drdpo_preference dataset. DPO, as introduced in the paper "Direct Preference Optimization: Your Language Model is Secretly a Reward Model," was the core training methodology. This approach directly optimizes a policy to satisfy preferences without explicitly training a reward model.
Good For
- Applications requiring models that generate helpful and aligned text.
- Tasks where preference-based fine-tuning is beneficial for output quality.
- Scenarios needing a model with a substantial context window for complex queries.