Hyeongwon/P19-split5-prob-3x-bs128-lr2e5-zero3-ep3
The Hyeongwon/P19-split5-prob-3x-bs128-lr2e5-zero3-ep3 is a 4 billion parameter language model, fine-tuned from Hyeongwon/Qwen3-4B-Base. This model was trained using Supervised Fine-Tuning (SFT) with the TRL library. It is designed for general text generation tasks, leveraging its base Qwen3 architecture and a 32768 token context length.
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Model Overview
Hyeongwon/P19-split5-prob-3x-bs128-lr2e5-zero3-ep3 is a 4 billion parameter language model, fine-tuned from the Hyeongwon/Qwen3-4B-Base architecture. This model was developed by Hyeongwon and trained using the TRL library, specifically employing Supervised Fine-Tuning (SFT) techniques.
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on user prompts.
- Fine-tuned Performance: Benefits from SFT training, which typically enhances performance on specific tasks or improves adherence to instructions.
- Base Model Foundation: Built upon the Qwen3-4B-Base, providing a robust foundation for language understanding and generation.
Training Details
The model's training procedure involved Supervised Fine-Tuning (SFT) using the TRL framework. Specific framework versions used include TRL 0.25.1, Transformers 4.57.3, Pytorch 2.9.1, Datasets 3.6.0, and Tokenizers 0.22.2. Training progress was monitored and can be visualized via Weights & Biases.
Good For
- General Text Generation: Suitable for various applications requiring text completion or response generation.
- Further Fine-tuning: Can serve as a strong base for additional fine-tuning on more specialized datasets or tasks.