ChuGyouk/3 is a 4 billion parameter instruction-tuned causal language model developed by ChuGyouk, fine-tuned from Qwen/Qwen3-4B-Instruct-2507. This model features a 40960 token context length and is specifically optimized for conversational AI tasks through supervised fine-tuning on the ChuGyouk/0120FINAL-AGUINAS-0.5k dataset. It is designed for generating human-like text responses in interactive applications.
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Overview
ChuGyouk/3 is a 4 billion parameter instruction-tuned language model, building upon the Qwen3-4B-Instruct-2507 base model. Developed by ChuGyouk, it has undergone supervised fine-tuning (SFT) using the TRL library on the specialized ChuGyouk/0120FINAL-AGUINAS-0.5k dataset. This training process aims to enhance its ability to follow instructions and generate coherent, contextually relevant responses.
Key Capabilities
- Instruction Following: Optimized for understanding and responding to user instructions effectively.
- Conversational AI: Fine-tuned on a dataset designed for interactive dialogue, making it suitable for chatbot and conversational agent applications.
- Extended Context: Features a substantial 40960 token context window, allowing it to process and generate longer, more complex interactions while maintaining coherence.
Training Details
The model was trained using the SFT method, leveraging TRL (Transformer Reinforcement Learning) for the fine-tuning process. The training run details are available for visualization on Weights & Biases. This focused training approach on a specific dataset differentiates it by tailoring its responses to particular conversational patterns and styles present in the training data.