Hyeongwon/joint_mimic3_p12_p19_split1_bs192_lr2e5_ep3
The Hyeongwon/joint_mimic3_p12_p19_split1_bs192_lr2e5_ep3 model is a 4 billion parameter language model developed by Hyeongwon, fine-tuned from Hyeongwon/Qwen3-4B-Base. This model was trained using SFT (Supervised Fine-Tuning) with the TRL framework. It is designed for general text generation tasks, building upon the capabilities of its base Qwen3-4B architecture.
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Model Overview
This model, joint_mimic3_p12_p19_split1_bs192_lr2e5_ep3, is a 4 billion parameter language model developed by Hyeongwon. It is a fine-tuned version of the Hyeongwon/Qwen3-4B-Base model, leveraging the Qwen3 architecture. The training process utilized Supervised Fine-Tuning (SFT) with the TRL (Transformer Reinforcement Learning) framework.
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on provided prompts.
- Fine-tuned Performance: Benefits from SFT to enhance its performance on specific tasks or domains, building upon its base model's general language understanding.
- Qwen3 Architecture: Inherits the robust capabilities of the Qwen3 model family, known for its efficiency and performance in various language tasks.
Training Details
The model was trained using the TRL framework, specifically version 0.25.1. Other framework versions used include Transformers 4.57.3, Pytorch 2.9.1, Datasets 3.6.0, and Tokenizers 0.22.2. The training run details are available for visualization on Weights & Biases.
Good For
- General-purpose text generation applications.
- Further fine-tuning for specialized downstream tasks.
- Research and development exploring SFT techniques on Qwen3-based models.