ChuGyouk/R2
ChuGyouk/R2 is an 8 billion parameter instruction-tuned causal language model, fine-tuned from ChuGyouk/Qwen3-8B-Base using the TRL framework. This model is designed for general text generation tasks, leveraging its base architecture and fine-tuning for improved conversational and instruction-following capabilities. With a 32768 token context length, it is suitable for applications requiring processing of longer inputs and generating coherent, extended responses.
Loading preview...
Overview
ChuGyouk/R2 is an 8 billion parameter language model, fine-tuned from the ChuGyouk/Qwen3-8B-Base architecture. This model has been specifically trained using the TRL (Transformer Reinforcement Learning) framework, indicating a focus on enhancing its instruction-following and conversational abilities through supervised fine-tuning (SFT).
Key Capabilities
- Instruction Following: Optimized through SFT to better understand and respond to user instructions.
- Text Generation: Capable of generating coherent and contextually relevant text based on prompts.
- Extended Context: Supports a context length of 32768 tokens, allowing for processing and generating longer sequences of text.
Training Details
The model's training process utilized SFT, a common technique for aligning language models with human preferences and instructions. The training run can be visualized via Weights & Biases, providing insights into its development. The fine-tuning was performed using TRL version 0.24.0, with Transformers 5.2.0 and Pytorch 2.10.0.
Good For
- General-purpose text generation tasks.
- Applications requiring models with strong instruction-following capabilities.
- Scenarios benefiting from a model that can handle longer input contexts.