Hyeongwon/P9-split1_only_answer_Qwen3-4B-Base_0402-01-2e-5 is a 4 billion parameter language model fine-tuned by Hyeongwon, based on the Qwen3-4B-Base architecture. This model has been specifically trained using Supervised Fine-Tuning (SFT) with TRL, focusing on generating direct answers. It is designed for tasks requiring concise and relevant responses, leveraging its 32768 token context length for processing substantial input.
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Model Overview
Hyeongwon/P9-split1_only_answer_Qwen3-4B-Base_0402-01-2e-5 is a 4 billion parameter language model derived from the Hyeongwon/Qwen3-4B-Base architecture. This model has undergone Supervised Fine-Tuning (SFT) using the TRL framework, indicating a specialized training approach to optimize its response generation.
Key Capabilities
- Fine-tuned for Direct Answers: The model's training methodology suggests an emphasis on producing concise and relevant answers, making it suitable for question-answering tasks where directness is preferred.
- Base Model: Built upon the Qwen3-4B-Base, it inherits the foundational capabilities of that architecture.
- Context Length: Features a 32768 token context length, allowing it to process and understand longer inputs before generating a response.
Training Details
The model was trained using SFT (Supervised Fine-Tuning) with the TRL library (version 0.25.1). The development environment included Transformers 4.57.3, Pytorch 2.6.0, Datasets 3.6.0, and Tokenizers 0.22.2. This specific training process aims to refine the model's ability to provide focused outputs.
Good For
- Question Answering: Ideal for applications requiring the model to extract and provide direct answers from given prompts.
- Response Generation: Suitable for scenarios where a model needs to generate specific, non-conversational responses.
- Integration with TRL Workflows: Developers familiar with TRL will find this model's training background consistent with that ecosystem.