ChuGyouk/5 is a 4 billion parameter language model developed by ChuGyouk, fine-tuned from ChuGyouk/Qwen3-4B-Base. It was trained using TRL on the ChuGyouk/0120FINAL-AGUINAS-1k dataset, featuring a 40960 token context length. This model is optimized for general text generation tasks, leveraging its fine-tuned base for improved performance.
Loading preview...
Model Overview
ChuGyouk/5 is a 4 billion parameter language model, fine-tuned by ChuGyouk. It is based on the ChuGyouk/Qwen3-4B-Base architecture and was specifically trained using the TRL (Transformer Reinforcement Learning) framework. The training utilized the ChuGyouk/0120FINAL-AGUINAS-1k dataset, indicating a focus on specific data characteristics for its fine-tuning process.
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on given prompts.
- Fine-tuned Performance: Benefits from a specialized fine-tuning process on a curated dataset, potentially leading to improved performance in its intended domain.
- Large Context Window: Supports a substantial context length of 40960 tokens, allowing it to process and generate longer sequences of text while maintaining coherence.
Training Details
The model underwent Supervised Fine-Tuning (SFT) using the TRL library. The training environment included specific versions of key frameworks:
- TRL: 0.24.0
- Transformers: 4.57.3
- Pytorch: 2.9.1
- Datasets: 4.3.0
- Tokenizers: 0.22.1
This model is suitable for developers looking for a 4B parameter model with a large context window, fine-tuned on a specific dataset for general text generation tasks.