Model Overview

Hyeongwon/P2-split4_prob_Qwen3-4B-Base_0312-01 is a 4 billion parameter language model, fine-tuned by Hyeongwon from the base Qwen3-4B-Base architecture. This model leverages the TRL (Transformer Reinforcement Learning) framework for its training, specifically employing Supervised Fine-Tuning (SFT).

Key Characteristics

• Base Model: Fine-tuned from Hyeongwon/Qwen3-4B-Base.
• Training Framework: Utilizes the TRL library for efficient fine-tuning.
• Training Method: Trained using Supervised Fine-Tuning (SFT).
• Context Length: Supports a substantial context window of 32768 tokens, allowing for processing and generating longer sequences of text.
• Development Environment: Developed with specific versions of key libraries including TRL 0.25.1, Transformers 4.57.3, Pytorch 2.6.0, Datasets 3.6.0, and Tokenizers 0.22.2.

Use Cases

This model is particularly well-suited for text generation tasks that benefit from a fine-tuned base model and a large context window. Its SFT training suggests an optimization for generating coherent and contextually appropriate responses, making it suitable for applications requiring detailed and nuanced text outputs.