Hyeongwon/P2-split1_prob_Qwen3-8B-Base_0312-01 is an 8 billion parameter language model, fine-tuned from ChuGyouk/Qwen3-8B-Base using the TRL framework. This model was trained with Supervised Fine-Tuning (SFT) and features a 32768 token context length. It is designed for text generation tasks, building upon the Qwen3 architecture.
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Model Overview
Hyeongwon/P2-split1_prob_Qwen3-8B-Base_0312-01 is an 8 billion parameter language model, fine-tuned from the ChuGyouk/Qwen3-8B-Base base model. This model was developed using the TRL (Transformer Reinforcement Learning) framework, specifically employing Supervised Fine-Tuning (SFT) as its training procedure. It leverages the Qwen3 architecture and supports a substantial context length of 32768 tokens.
Key Capabilities
- Text Generation: Optimized for generating coherent and contextually relevant text based on user prompts.
- Fine-tuned Performance: Benefits from SFT, suggesting improved performance on specific tasks or domains compared to its base model.
- TRL Framework: Built with TRL, indicating potential for further reinforcement learning applications or advanced fine-tuning techniques.
Usage
This model is suitable for various text generation applications. A quick start example demonstrates its use with the transformers pipeline for generating responses to questions. Developers can integrate it into their projects using standard Hugging Face transformers library practices.
Training Details
The model's training process utilized TRL version 0.25.1, Transformers 4.57.3, Pytorch 2.6.0, Datasets 3.6.0, and Tokenizers 0.22.2. Further details on the training run can be visualized via Weights & Biases.