Hyeongwon/P19-split3-prob-6x-bs128-lr2e5-zero3-ep3
Hyeongwon/P19-split3-prob-6x-bs128-lr2e5-zero3-ep3 is a 4 billion parameter language model, fine-tuned from Hyeongwon/Qwen3-4B-Base with a 32K context length. This model was trained using Supervised Fine-Tuning (SFT) with the TRL library. It is designed for general text generation tasks, building upon the base capabilities of the Qwen3 architecture. The training procedure focused on specific fine-tuning objectives, making it suitable for applications requiring a refined base model.
Loading preview...
Model Overview
Hyeongwon/P19-split3-prob-6x-bs128-lr2e5-zero3-ep3 is a 4 billion parameter language model, fine-tuned from the Hyeongwon/Qwen3-4B-Base architecture. This model leverages a substantial 32,768 token context window, making it capable of processing and generating longer sequences of text. The fine-tuning process was conducted using Supervised Fine-Tuning (SFT), a common method for adapting pre-trained language models to specific tasks or datasets.
Training Details
The model's training utilized the TRL (Transformer Reinforcement Learning) library, specifically version 0.25.1. Key framework versions involved in its development include Transformers 4.57.3, Pytorch 2.9.1, Datasets 3.6.0, and Tokenizers 0.22.2. The training procedure was meticulously tracked and can be visualized via Weights & Biases, providing transparency into the fine-tuning process.
Key Capabilities
- General Text Generation: Capable of generating coherent and contextually relevant text based on given prompts.
- Long Context Understanding: Benefits from a 32K context length, allowing for better comprehension and generation over extended inputs.
- Fine-tuned Performance: Optimized through SFT to enhance its performance for specific applications, building on the robust foundation of the Qwen3-4B-Base model.
Intended Use
This model is suitable for developers and researchers looking for a fine-tuned 4B parameter model for various natural language processing tasks, particularly those that can benefit from its SFT-driven optimizations and extended context window. It serves as a strong base for further experimentation or deployment in applications requiring a refined general-purpose language model.