Model Overview
This model, named Hyeongwon/PH_det_sft_FC_swap_labewise_data_oversampling_bf16_lr0.00002_context_12k-Qwen3-8B-Base, is an 8 billion parameter language model. It is a fine-tuned variant of the ChuGyouk/Qwen3-8B-Base model, developed by Hyeongwon.
Key Capabilities
- Base Model Enhancement: Builds upon the Qwen3-8B-Base architecture, inheriting its foundational language understanding and generation capabilities.
- Supervised Fine-Tuning (SFT): Utilizes SFT with the TRL framework for specialized training, indicating a focus on improving performance for specific tasks or data distributions.
- Data Oversampling: Incorporates 'labewise data oversampling' during training, suggesting an optimization for handling imbalanced datasets or improving performance on underrepresented categories.
- Extended Context Window: Features a substantial context length of 32768 tokens, enabling the model to process and generate longer, more coherent texts while maintaining contextual awareness.
Training Details
The model was trained using the TRL (Transformer Reinforcement Learning) library, specifically employing Supervised Fine-Tuning. The training process involved a learning rate of 0.00002 and bf16 precision. Further details on the training run can be visualized via Weights & Biases.
Good For
- General Text Generation: Suitable for a wide range of text generation tasks, from answering questions to creative writing, given its base model and fine-tuning.
- Applications Requiring Context: Its 32768 token context window makes it particularly effective for tasks that demand understanding and generating long-form content or maintaining conversational history.
- Research and Development: Can serve as a strong foundation for further fine-tuning or experimentation, especially for tasks where data imbalance is a concern, due to its specific oversampling training methodology.