Hyeongwon/P9-split1_only_answer_Qwen3-4B-Base_0402-01-1e-5 is a 4 billion parameter language model developed by Hyeongwon, fine-tuned from Qwen3-4B-Base. This model is specifically trained using Supervised Fine-Tuning (SFT) with TRL, focusing on generating direct answers. It is optimized for tasks requiring concise, direct responses to user queries, leveraging its 32768 token context length for comprehensive understanding.
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Overview
Hyeongwon/P9-split1_only_answer_Qwen3-4B-Base_0402-01-1e-5 is a 4 billion parameter language model, fine-tuned by Hyeongwon from the base model Qwen3-4B-Base. This model was developed using Supervised Fine-Tuning (SFT) with the TRL library, indicating a focus on specific task performance rather than broad generative capabilities. It is designed to provide direct answers, making it suitable for applications where concise and relevant responses are paramount.
Key Capabilities
- Direct Answer Generation: Specialized in producing straightforward answers to questions.
- Fine-tuned Performance: Leverages SFT for optimized performance on its intended task.
- Base Model: Built upon the Qwen3-4B-Base architecture, providing a solid foundation for language understanding.
Training Details
The model underwent Supervised Fine-Tuning (SFT) using the TRL framework (version 0.25.1). The training process utilized Transformers (4.57.3), Pytorch (2.6.0), Datasets (3.6.0), and Tokenizers (0.22.2).
Use Cases
This model is particularly well-suited for applications requiring precise and direct responses, such as:
- Question-answering systems where brevity is valued.
- Chatbots designed to provide factual information without extensive conversational filler.
- Integration into systems that need to extract and present specific information from prompts.