Kyleyee/rDPO_hh-seed5
Kyleyee/rDPO_hh-seed5 is a 1.5 billion parameter language model fine-tuned from Kyleyee/Qwen2.5-1.5B-sft-hh-3e with a 32768 token context length. This model was trained using Direct Preference Optimization (DPO) on a helpfulness preference dataset. It is designed to generate more helpful and aligned responses, making it suitable for conversational AI and instruction-following tasks.
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Model Overview
Kyleyee/rDPO_hh-seed5 is a 1.5 billion parameter language model, building upon the base of Kyleyee/Qwen2.5-1.5B-sft-hh-3e. It features a substantial context length of 32768 tokens, allowing for processing longer inputs and generating more coherent, extended responses.
Training Methodology
This model was fine-tuned using Direct Preference Optimization (DPO), a method introduced in the paper "Direct Preference Optimization: Your Language Model is Secretly a Reward Model." The training utilized the Kyleyee/train_data_Helpful_drdpo_preference dataset, specifically designed to enhance the model's helpfulness and alignment. The training process was implemented using the TRL library.
Key Characteristics
- Parameter Count: 1.5 billion parameters.
- Context Length: 32768 tokens.
- Optimization: Fine-tuned with DPO for improved helpfulness and preference alignment.
- Base Model: Derived from Kyleyee/Qwen2.5-1.5B-sft-hh-3e.
Use Cases
This model is particularly well-suited for applications requiring:
- Helpful and aligned responses: Its DPO training on a helpfulness dataset makes it effective for generating user-friendly and constructive outputs.
- Conversational AI: Capable of engaging in extended dialogues due to its large context window.
- Instruction following: Designed to better understand and execute user instructions.