Hyeongwon/P12-frac0p05-fullft-lr5e5-ep6
Hyeongwon/P12-frac0p05-fullft-lr5e5-ep6 is a 4 billion parameter language model, fine-tuned from Hyeongwon/Qwen3-4B-Base using the TRL framework. This model is designed for general text generation tasks, leveraging its base architecture and fine-tuning for improved performance. It offers a substantial 32,768 token context window, making it suitable for processing longer inputs and generating coherent, extended responses.
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Model Overview
Hyeongwon/P12-frac0p05-fullft-lr5e5-ep6 is a 4 billion parameter language model, fine-tuned from the Hyeongwon/Qwen3-4B-Base architecture. This model was developed using the TRL (Transformer Reinforcement Learning) framework, specifically through a Supervised Fine-Tuning (SFT) training procedure. It is designed for various text generation tasks, building upon the capabilities of its base model.
Key Capabilities
- Text Generation: Capable of generating human-like text based on given prompts.
- Large Context Window: Features a 32,768 token context length, allowing it to process and generate longer, more complex sequences while maintaining coherence.
- Fine-tuned Performance: Benefits from specific fine-tuning, which typically enhances its ability to follow instructions and produce relevant outputs compared to a base model.
Training Details
The model's training involved the SFT method, utilizing TRL version 0.25.1, Transformers 4.57.3, Pytorch 2.7.0+cu128, Datasets 3.6.0, and Tokenizers 0.22.2. Further details on the training process can be explored via the associated Weights & Biases run.
Good For
- Developers looking for a 4B parameter model with a large context window for general text generation.
- Applications requiring models fine-tuned for instruction following or specific conversational styles.
- Experimentation with models trained using the TRL framework.