Hyeongwon/P2-split1_only_answer_Qwen3-4B-Base_0501-bs64-epoch6
Hyeongwon/P2-split1_only_answer_Qwen3-4B-Base_0501-bs64-epoch6 is a 4 billion parameter language model fine-tuned from Hyeongwon/Qwen3-4B-Base using TRL. This model is specifically trained with Supervised Fine-Tuning (SFT) to generate direct answers, making it suitable for question-answering tasks where concise responses are preferred. It leverages a 32K context length, providing ample capacity for processing detailed prompts.
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Model Overview
Hyeongwon/P2-split1_only_answer_Qwen3-4B-Base_0501-bs64-epoch6 is a 4 billion parameter language model, fine-tuned from the base model Hyeongwon/Qwen3-4B-Base. This model has undergone Supervised Fine-Tuning (SFT) using the TRL library, focusing on generating direct and concise answers to user prompts.
Key Capabilities
- Direct Answer Generation: Optimized to provide straightforward answers, making it suitable for specific question-answering scenarios.
- Base Model: Built upon the Qwen3-4B-Base architecture, inheriting its foundational language understanding capabilities.
- Training Framework: Fine-tuned using the TRL (Transformer Reinforcement Learning) library, indicating a focus on improving response quality through advanced training techniques.
- Context Length: Supports a context length of 32,768 tokens, allowing it to process and understand longer and more complex queries.
Use Cases
This model is particularly well-suited for applications requiring:
- Concise Q&A Systems: Ideal for chatbots or virtual assistants where users expect direct and unambiguous answers.
- Information Extraction: Can be used to extract specific pieces of information from text by formulating questions that lead to direct answers.
- Automated Response Generation: Suitable for generating automated responses in scenarios where brevity and accuracy are paramount.