Hyeongwon/P12-frac0p05-fullft-lr1e5-ep6
Hyeongwon/P12-frac0p05-fullft-lr1e5-ep6 is a 4 billion parameter language model developed by Hyeongwon, fine-tuned from Hyeongwon/Qwen3-4B-Base. This model was trained using Supervised Fine-Tuning (SFT) with the TRL framework, offering a 32768 token context length. It is designed for general text generation tasks, building upon the Qwen3 architecture.
Loading preview...
Model Overview
Hyeongwon/P12-frac0p05-fullft-lr1e5-ep6 is a 4 billion parameter language model, fine-tuned by Hyeongwon from its base model, Hyeongwon/Qwen3-4B-Base. This model leverages the Qwen3 architecture and was developed using the TRL (Transformer Reinforcement Learning) framework, specifically through Supervised Fine-Tuning (SFT).
Key Capabilities
- Text Generation: Excels at generating coherent and contextually relevant text based on provided prompts.
- Fine-tuned Performance: Benefits from SFT, which typically enhances performance on specific tasks or improves general instruction following.
- Large Context Window: Features a substantial context length of 32768 tokens, allowing it to process and generate longer sequences of text while maintaining context.
Training Details
The model's training procedure involved Supervised Fine-Tuning (SFT) using the TRL library (version 0.25.1). The development environment included Transformers 4.57.3, Pytorch 2.7.0+cu128, Datasets 3.6.0, and Tokenizers 0.22.2. Further details on the training run can be visualized via Weights & Biases.
Good For
- General Text Generation: Suitable for a wide range of applications requiring text output, such as creative writing, question answering, and conversational AI.
- Research and Experimentation: Provides a solid base for further fine-tuning or experimentation within the Qwen3-4B family, particularly for those interested in SFT methodologies.