Kyleyee/cDPO_hh-seed3
Kyleyee/cDPO_hh-seed3 is a 1.5 billion parameter language model developed by Kyleyee, fine-tuned from Kyleyee/Qwen2.5-1.5B-sft-hh-3e. It was trained using Direct Preference Optimization (DPO) on the Kyleyee/train_data_Helpful_drdpo_preference dataset, specializing in generating helpful and preferred responses. With a context length of 32768 tokens, this model is optimized for conversational AI and instruction-following tasks where response quality and alignment with human preferences are critical.
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Overview
Kyleyee/cDPO_hh-seed3 is a 1.5 billion parameter language model, fine-tuned by Kyleyee from the base model Kyleyee/Qwen2.5-1.5B-sft-hh-3e. This model leverages the Direct Preference Optimization (DPO) method, as introduced in the paper "Direct Preference Optimization: Your Language Model is Secretly a Reward Model," to align its outputs with human preferences. It was specifically trained on the Kyleyee/train_data_Helpful_drdpo_preference dataset, focusing on generating helpful and high-quality responses.
Key Capabilities
- Preference-aligned Generation: Optimized using DPO to produce responses that are preferred by humans, enhancing helpfulness and quality.
- Instruction Following: Designed to effectively follow user instructions and generate relevant outputs.
- Conversational AI: Suitable for dialogue systems and chatbots where nuanced and helpful interactions are desired.
- Efficient Size: At 1.5 billion parameters, it offers a balance between performance and computational efficiency.
- Extended Context: Supports a context length of 32768 tokens, allowing for processing longer inputs and maintaining coherence over extended conversations.
Good for
- Chatbot Development: Creating more helpful and engaging conversational agents.
- Assistant Models: Building AI assistants that provide preferred and aligned responses.
- Preference-based Fine-tuning: Demonstrating the application of DPO for aligning language models.
- Resource-constrained Environments: Deploying a capable model in scenarios where larger models might be prohibitive.