Aletheia-Bench/DPO-Think-7B
Aletheia-Bench/DPO-Think-7B is a 7.6 billion parameter language model fine-tuned from deepseek-ai/DeepSeek-R1-Distill-Qwen-7B. This model was trained using Direct Preference Optimization (DPO) with the TRL framework. It is designed to enhance response quality and alignment through preference-based learning. The model supports a context length of 32768 tokens, making it suitable for tasks requiring extensive contextual understanding.
Loading preview...
Model Overview
Aletheia-Bench/DPO-Think-7B is a 7.6 billion parameter language model, fine-tuned from the deepseek-ai/DeepSeek-R1-Distill-Qwen-7B base model. This model leverages Direct Preference Optimization (DPO), a method that directly optimizes a language model's policy to align with human preferences without the need for a separate reward model. The training was conducted using the TRL (Transformer Reinforcement Learning) framework.
Key Characteristics
- Base Model: Fine-tuned from
deepseek-ai/DeepSeek-R1-Distill-Qwen-7B. - Training Method: Utilizes Direct Preference Optimization (DPO) for improved alignment and response quality.
- Framework: Trained with the TRL library, version 0.24.0.
- Parameter Count: 7.6 billion parameters.
- Context Length: Supports a substantial context window of 32768 tokens.
Use Cases
This model is particularly well-suited for applications where generating high-quality, preference-aligned text is crucial. Its DPO training aims to produce responses that are more helpful, harmless, and honest, making it a strong candidate for:
- Conversational AI: Enhancing chatbot responses and dialogue systems.
- Content Generation: Producing aligned and coherent text for various purposes.
- Instruction Following: Generating outputs that better adhere to user instructions and preferences.