Hyeongwon/P12-split3-one-sided-bs64-lr2e5-zero3-ep3
Hyeongwon/P12-split3-one-sided-bs64-lr2e5-zero3-ep3 is a 4 billion parameter causal language model fine-tuned by Hyeongwon from the Qwen3-4B-Base architecture. This model was trained using Supervised Fine-Tuning (SFT) with the TRL framework, focusing on specific conversational capabilities. It is designed for text generation tasks, particularly in response to user prompts, leveraging its 32768 token context length.
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Model Overview
Hyeongwon/P12-split3-one-sided-bs64-lr2e5-zero3-ep3 is a 4 billion parameter language model developed by Hyeongwon. It is a fine-tuned variant of the Qwen3-4B-Base model, specifically optimized through Supervised Fine-Tuning (SFT) using the TRL framework. This model is designed for general text generation tasks, offering a substantial 32768 token context window.
Key Capabilities
- Text Generation: Excels at generating coherent and contextually relevant text based on user prompts.
- Fine-tuned Performance: Benefits from SFT training, which typically enhances performance on specific conversational or instruction-following tasks.
- Large Context Window: Supports a 32768 token context length, allowing for processing and generating longer sequences of text while maintaining context.
Training Details
The model was trained using the TRL (Transformer Reinforcement Learning) library, indicating a focus on refining its conversational abilities. The training procedure involved SFT, a common method for adapting base models to specific instruction-following or dialogue generation tasks. Key framework versions used include TRL 0.25.1, Transformers 4.57.3, and Pytorch 2.9.1.
Good For
- Conversational AI: Suitable for applications requiring interactive dialogue or response generation.
- General Text Generation: Can be used for various tasks where generating human-like text from a given prompt is required.
- Research and Development: Provides a fine-tuned base for further experimentation or adaptation to more specialized tasks.