ChuGyouk/R1_2_4b is a 4 billion parameter language model fine-tuned from ChuGyouk/Qwen3-4B-Base using SFT (Supervised Fine-Tuning) with TRL. This model is designed for general text generation tasks, leveraging its base architecture and fine-tuning for improved conversational and response generation capabilities. It offers a 32768 token context length, making it suitable for processing longer inputs and generating coherent, extended outputs.
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Model Overview
ChuGyouk/R1_2_4b is a 4 billion parameter language model developed by ChuGyouk. It is a fine-tuned variant of the ChuGyouk/Qwen3-4B-Base model, specifically trained using Supervised Fine-Tuning (SFT) with the TRL (Transformer Reinforcement Learning) library. This training approach aims to enhance the model's ability to generate relevant and coherent text based on user prompts.
Key Capabilities
- Text Generation: Capable of generating human-like text responses to a variety of prompts.
- Context Handling: Supports a substantial context length of 32768 tokens, allowing for processing and generating longer passages of text.
- Fine-tuned Performance: Benefits from SFT training, which typically improves instruction following and response quality compared to base models.
Training Details
The model was trained using the TRL library (version 0.24.0) in conjunction with Transformers (version 5.2.0) and PyTorch (version 2.10.0). The SFT training procedure focuses on optimizing the model's output for specific tasks or conversational styles.
Good For
- Conversational AI: Generating responses in chat-like interactions.
- Content Creation: Assisting with drafting text, summaries, or creative writing.
- Question Answering: Providing informative answers to direct questions, especially those requiring contextual understanding from longer inputs.