Phantomcloak19/qwen2.5-3b-dpo
Phantomcloak19/qwen2.5-3b-dpo is a 3.1 billion parameter causal language model based on the Qwen2.5-3B-Instruct architecture. This model represents the DPO (Direct Preference Optimization) phase of the HorusLLM sequential training pipeline, following an initial Supervised Fine-Tuning (SFT) stage. It is specifically optimized through DPO, indicating a focus on aligning its outputs with human preferences and instructions, making it suitable for applications requiring nuanced and preferred responses.
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Overview
Phantomcloak19/qwen2.5-3b-dpo is a 3.1 billion parameter language model derived from the Qwen/Qwen2.5-3B-Instruct base model. It is a key component of the HorusLLM sequential training pipeline, specifically representing the DPO (Direct Preference Optimization) phase. This DPO stage follows an initial Supervised Fine-Tuning (SFT) phase and precedes a Safety-GRPO phase, indicating a structured approach to model development.
Key Characteristics
- Base Model: Built upon the robust
Qwen/Qwen2.5-3B-Instructarchitecture. - Training Phase: Optimized using Direct Preference Optimization (DPO), which aims to align the model's responses more closely with human preferences and instructions.
- Pipeline Integration: Part of a multi-stage training process (SFT → DPO → Safety-GRPO) designed for comprehensive model refinement.
Good For
- Applications requiring models fine-tuned for human preference alignment.
- Use cases where instruction following and generating preferred responses are critical.
- Further experimentation or integration into pipelines that benefit from a DPO-optimized base.