Hyeongwon/P19-split1-prob-3x-bs64-lr2e5-zero3-ep3
Hyeongwon/P19-split1-prob-3x-bs64-lr2e5-zero3-ep3 is a 4 billion parameter language model fine-tuned from Hyeongwon/Qwen3-4B-Base. This model was trained using the TRL framework with a supervised fine-tuning (SFT) approach. It is designed for general text generation tasks, building upon the capabilities of its Qwen3-4B-Base foundation.
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Overview
This model, named Hyeongwon/P19-split1-prob-3x-bs64-lr2e5-zero3-ep3, is a 4 billion parameter language model. It is a fine-tuned variant of the Hyeongwon/Qwen3-4B-Base model, indicating its foundation in the Qwen3 architecture. The fine-tuning process utilized the TRL (Transformer Reinforcement Learning) framework, specifically employing a Supervised Fine-Tuning (SFT) methodology.
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on user prompts.
- Fine-tuned Performance: Benefits from additional training on top of a robust base model, potentially enhancing its performance for specific applications.
- TRL Framework: Developed using the TRL library, which is designed for transformer reinforcement learning, suggesting potential for further adaptation or specialized tasks.
Training Details
The model's training involved a supervised fine-tuning (SFT) approach. The development environment included specific versions of key frameworks:
- TRL: 0.25.1
- Transformers: 4.57.3
- Pytorch: 2.9.1
- Datasets: 3.6.0
- Tokenizers: 0.22.2
Good For
- Developers looking for a fine-tuned 4B parameter model based on Qwen3 for various text generation tasks.
- Experimentation with models trained using the TRL framework and SFT methods.
- Applications requiring a moderately sized language model with a 32768 token context length.