Hyeongwon/PS_only_answer_Qwen3-4B-Base_0328-01-1e-5-seed46 is a 4 billion parameter language model, fine-tuned from Hyeongwon/Qwen3-4B-Base using Supervised Fine-Tuning (SFT) with the TRL framework. This model is specifically designed for generating direct answers, as indicated by its name "PS_only_answer". It is optimized for tasks requiring concise and focused responses, making it suitable for question-answering systems where brevity and directness are prioritized.
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Model Overview
This model, Hyeongwon/PS_only_answer_Qwen3-4B-Base_0328-01-1e-5-seed46, is a 4 billion parameter language model derived from the Hyeongwon/Qwen3-4B-Base architecture. It has undergone Supervised Fine-Tuning (SFT) using the Hugging Face TRL library, a framework for Transformer Reinforcement Learning.
Key Capabilities
- Direct Answer Generation: The model's naming convention, "PS_only_answer", suggests a specialization in producing concise and direct responses, likely optimized for scenarios where only the answer is required without additional conversational filler.
- Fine-tuned Performance: Built upon a Qwen3-4B-Base model, this version benefits from targeted SFT, which typically enhances performance on specific tasks compared to its base counterpart.
Training Details
The model was trained using Supervised Fine-Tuning (SFT) with the TRL framework. The training environment utilized specific versions of key libraries:
- TRL: 0.25.1
- Transformers: 4.57.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.22.2
Use Cases
This model is particularly well-suited for applications requiring straightforward and focused answers. Potential use cases include:
- Question Answering Systems: Where the goal is to extract and present only the most relevant answer to a query.
- Automated Response Generation: For systems that need to provide direct information without extensive dialogue.
- Content Summarization: Generating brief, answer-like summaries from longer texts.