Hyeongwon/P2-split2_prob_rg_v2_Qwen3-4B-Base

Model Overview

Hyeongwon/P2-split2_prob_rg_v2_Qwen3-4B-Base is a 4 billion parameter language model developed by Hyeongwon. It is a fine-tuned iteration of the base Qwen3-4B model, specifically optimized through Supervised Fine-Tuning (SFT) using the TRL framework. This model is designed to handle a context length of 32,768 tokens, allowing for processing and generation of relatively extensive text.

Key Capabilities

• Fine-tuned Performance: Enhanced from its base model through SFT, suggesting improved performance on tasks aligned with its training data.
• TRL Framework: Utilizes the Transformer Reinforcement Learning (TRL) library for its training procedure, indicating a focus on advanced fine-tuning techniques.
• Moderate Context Window: Supports a 32K token context length, suitable for tasks requiring understanding and generation over longer passages of text.

Training Details

The model was trained using SFT, leveraging the TRL framework (version 0.25.1). Other framework versions used include Transformers 4.57.3, Pytorch 2.6.0, Datasets 3.6.0, and Tokenizers 0.22.2. Further details on the training process can be visualized via the associated Weights & Biases run.

Good For

• Specific Fine-tuned Applications: Ideal for use cases that align with the specific SFT objectives it was trained on.
• Research and Development: Provides a foundation for further experimentation and fine-tuning, particularly for those working with the TRL library.
• Applications requiring moderate context: Its 32K context window makes it suitable for tasks like summarization, question answering, or content generation where input length is a factor.