ChuGyouk/2 is a 4 billion parameter instruction-tuned causal language model developed by ChuGyouk, fine-tuned from Qwen/Qwen3-4B-Instruct-2507. It features a substantial 40960-token context length, making it suitable for tasks requiring extensive contextual understanding. This model is specifically optimized for conversational AI and question-answering based on its fine-tuning dataset.
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Overview
ChuGyouk/2 is a 4 billion parameter instruction-tuned language model, built upon the Qwen/Qwen3-4B-Instruct-2507 architecture. It has been fine-tuned by ChuGyouk using the ChuGyouk/0120FINAL-AGUINAS-1k dataset, leveraging the TRL (Transformer Reinforcement Learning) library for its training process.
Key Capabilities
- Instruction Following: Designed to respond to user prompts and instructions effectively, as demonstrated by its fine-tuning on an instruction-based dataset.
- Extended Context Window: Benefits from the base model's 40960-token context length, allowing for processing and generating longer sequences of text while maintaining coherence.
- Conversational AI: Optimized for interactive dialogue and question-answering scenarios due to its instruction-tuned nature and specific training data.
Training Details
The model was trained using Supervised Fine-Tuning (SFT) with TRL version 0.24.0. The training process can be visualized via Weights & Biases, linked in the original model card. This fine-tuning process aims to enhance its performance on specific conversational and instructional tasks.