Model Overview

Hyeongwon/P2-split2_prob_Qwen3-4B-Base_0312-01 is a 4 billion parameter language model, derived from the Hyeongwon/Qwen3-4B-Base architecture. This model has undergone Supervised Fine-Tuning (SFT) using the TRL library, a framework for Transformer Reinforcement Learning.

Key Characteristics

• Base Model: Fine-tuned from Hyeongwon/Qwen3-4B-Base.
• Training Method: Utilizes Supervised Fine-Tuning (SFT).
• Context Length: Supports a substantial context window of 32768 tokens.
• Frameworks: Developed with TRL (0.25.1), Transformers (4.57.3), Pytorch (2.6.0), Datasets (3.6.0), and Tokenizers (0.22.2).

Intended Use Cases

This model is suitable for various text generation tasks, leveraging its SFT training to produce coherent and contextually relevant outputs. Developers can integrate it into applications requiring conversational AI, content creation, or other natural language processing functionalities. A quick start example is provided for text generation using the Hugging Face transformers pipeline.