Phantomcloak19/qwen3-4b-dpo
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 30, 2026Architecture:Transformer Cold
Phantomcloak19/qwen3-4b-dpo is a 4 billion parameter language model, part of the HorusLLM sequential training pipeline, specifically after the DPO (Direct Preference Optimization) phase. Based on the Qwen3-4B architecture, this model has undergone preference alignment to enhance its conversational quality and adherence to user instructions. It is optimized for generating more helpful and harmless responses, making it suitable for general-purpose conversational AI applications.
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Phantomcloak19/qwen3-4b-dpo: DPO-Aligned Qwen3-4B Model
This model, Phantomcloak19/qwen3-4b-dpo, represents a significant step in the HorusLLM sequential training pipeline. It is a 4 billion parameter model built upon the robust Qwen/Qwen3-4B base architecture.
Key Capabilities
- Direct Preference Optimization (DPO): This model has completed the DPO phase, which is crucial for aligning its outputs with human preferences. This typically results in responses that are more helpful, harmless, and follow instructions better than models prior to this phase.
- Enhanced Conversational Quality: The DPO training aims to improve the model's ability to engage in natural and coherent dialogue, making it more suitable for interactive applications.
- Foundation for Further Refinement: As an intermediate step in the HorusLLM pipeline (SFT → DPO → Safety-GRPO), this model provides a strong foundation for subsequent safety and advanced alignment stages.
Good For
- General-purpose conversational AI: Its DPO alignment makes it well-suited for chatbots, virtual assistants, and other applications requiring human-like interaction.
- Instruction-following tasks: The preference tuning helps the model better understand and execute user commands and requests.
- As a base for further fine-tuning: Developers looking to build specialized models can use this DPO-aligned version as an excellent starting point for domain-specific adaptations.