ChuGyouk/F_R1_4b is a 4 billion parameter language model fine-tuned from ChuGyouk/Qwen3-4B-Base. This model was trained using the TRL library with a supervised fine-tuning (SFT) approach. It is designed for general text generation tasks, building upon its Qwen3 base architecture.
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Model Overview
ChuGyouk/F_R1_4b is a 4 billion parameter language model developed by ChuGyouk. It is a fine-tuned iteration of the ChuGyouk/Qwen3-4B-Base model, leveraging the Qwen3 architecture. The model was trained using the Transformer Reinforcement Learning (TRL) library, specifically employing a Supervised Fine-Tuning (SFT) methodology.
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on given prompts.
- Instruction Following: Designed to respond to user queries and instructions, as demonstrated by the quick start example.
- Base Model Enhancement: Builds upon the foundational capabilities of the Qwen3-4B-Base model through targeted fine-tuning.
Training Details
The fine-tuning process utilized TRL version 0.24.0, with Transformers version 5.2.0, PyTorch 2.10.0, Datasets 4.3.0, and Tokenizers 0.22.2. The training procedure was monitored and visualized using Weights & Biases, indicating a structured and tracked development process.
Good For
- General Purpose Text Generation: Suitable for a variety of tasks requiring text output, such as answering questions, creative writing, or conversational AI.
- Research and Development: Provides a fine-tuned 4B parameter model for further experimentation or integration into larger systems.