Hyeongwon/P19-split5-prob-6x-bs128-lr2e5-zero3-ep3
Hyeongwon/P19-split5-prob-6x-bs128-lr2e5-zero3-ep3 is a 4 billion parameter causal language model developed by Hyeongwon, fine-tuned from Hyeongwon/Qwen3-4B-Base. This model was trained using Supervised Fine-Tuning (SFT) with the TRL framework. It is designed for text generation tasks, building upon the base capabilities of the Qwen3 architecture.
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Model Overview
Hyeongwon/P19-split5-prob-6x-bs128-lr2e5-zero3-ep3 is a 4 billion parameter language model developed by Hyeongwon. It is a fine-tuned variant of the Hyeongwon/Qwen3-4B-Base model, leveraging the Qwen3 architecture.
Training Details
This model was trained using Supervised Fine-Tuning (SFT), a common method for adapting pre-trained language models to specific tasks or behaviors. The training process utilized the TRL (Transformer Reinforcement Learning) framework, specifically version 0.25.1. Key software versions used during training include Transformers 4.57.3, Pytorch 2.9.1, Datasets 3.6.0, and Tokenizers 0.22.2.
Key Capabilities
- Text Generation: The model is capable of generating coherent and contextually relevant text based on given prompts.
- Fine-tuned Performance: As an SFT model, it is expected to exhibit improved performance on tasks aligned with its fine-tuning data compared to its base model.
Intended Use
This model is suitable for various text generation applications where a 4 billion parameter model provides a balance between performance and computational efficiency. Developers can integrate it into their projects using the Hugging Face transformers library for tasks such as conversational AI, content creation, or question answering.