Hyeongwon/P2-split2_only_answer_Qwen3-4B-Base_0505-bs64-epoch6-lr1e5
The Hyeongwon/P2-split2_only_answer_Qwen3-4B-Base_0505-bs64-epoch6-lr1e5 is a 4 billion parameter language model, fine-tuned from Hyeongwon/Qwen3-4B-Base using TRL. This model is specifically trained to generate direct answers, making it suitable for question-answering tasks where concise, focused responses are desired. It leverages a 32K context length, providing ample capacity for processing detailed prompts.
Loading preview...
Model Overview
The Hyeongwon/P2-split2_only_answer_Qwen3-4B-Base_0505-bs64-epoch6-lr1e5 is a 4 billion parameter language model, fine-tuned from the Hyeongwon/Qwen3-4B-Base architecture. This model has been trained using the TRL (Transformer Reinforcement Learning) framework, specifically employing Supervised Fine-Tuning (SFT) as its training procedure.
Key Capabilities
- Direct Answer Generation: The model is fine-tuned to provide concise and direct answers, making it optimized for specific question-answering scenarios.
- Base Model: Built upon the Qwen3-4B-Base, it inherits the foundational capabilities of that architecture.
- Context Length: Supports a context length of 32,768 tokens, allowing for processing of substantial input queries.
Training Details
The model underwent a Supervised Fine-Tuning (SFT) process. The training utilized specific framework versions including TRL 0.25.1, Transformers 4.57.3, Pytorch 2.9.1, Datasets 3.6.0, and Tokenizers 0.22.2. Further details on the training run can be explored via its Weights & Biases project page.
Good For
- Applications requiring focused and direct answers to questions.
- Integration into systems where concise output is preferred over verbose responses.
- Developers familiar with the TRL framework for fine-tuning.