Hyeongwon/P2-split2_prob_rg_v2_Qwen3-4B-Base-0416
Hyeongwon/P2-split2_prob_rg_v2_Qwen3-4B-Base-0416 is a 4 billion parameter language model developed by Hyeongwon, fine-tuned from the Qwen3-4B-Base architecture. This model was trained using Supervised Fine-Tuning (SFT) with the TRL framework. It is designed for general text generation tasks, building upon its base model's capabilities.
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Model Overview
Hyeongwon/P2-split2_prob_rg_v2_Qwen3-4B-Base-0416 is a 4 billion parameter language model, representing a fine-tuned iteration of the Hyeongwon/Qwen3-4B-Base architecture. This model leverages the Qwen3-4B-Base as its foundation, indicating a robust base for various natural language processing tasks. The fine-tuning process was conducted using the TRL (Transformer Reinforcement Learning) framework, specifically employing Supervised Fine-Tuning (SFT) techniques.
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on given prompts.
- Fine-tuned Performance: Benefits from SFT, suggesting improved performance on specific tasks or domains compared to its base model.
- Qwen3 Architecture: Built on the Qwen3 family, known for its general language understanding and generation abilities.
Training Details
The model's training utilized the TRL framework (version 0.25.1) alongside Transformers (4.57.3), Pytorch (2.6.0), Datasets (3.6.0), and Tokenizers (0.22.2). This setup indicates a standard and well-supported training environment for large language models. The fine-tuning approach focuses on supervised learning to adapt the base model to specific objectives.