Hyeongwon/P12-split1-one-sided-bs64-lr2e5-zero3-ep3
Hyeongwon/P12-split1-one-sided-bs64-lr2e5-zero3-ep3 is a 4 billion parameter language model, fine-tuned from Hyeongwon/Qwen3-4B-Base using TRL. This model is designed for text generation tasks, particularly conversational responses, and supports a context length of 32768 tokens. Its training with Supervised Fine-Tuning (SFT) aims to enhance its ability to generate coherent and relevant text based on user prompts.
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Model Overview
Hyeongwon/P12-split1-one-sided-bs64-lr2e5-zero3-ep3 is a 4 billion parameter language model, fine-tuned from the Hyeongwon/Qwen3-4B-Base architecture. This model leverages the TRL (Transformer Reinforcement Learning) library for its training process, specifically utilizing Supervised Fine-Tuning (SFT).
Key Capabilities
- Text Generation: Optimized for generating coherent and contextually relevant text based on given prompts.
- Conversational AI: Suitable for tasks requiring interactive dialogue and response generation, as demonstrated by its quick start example.
- Large Context Window: Supports a substantial context length of 32768 tokens, allowing for processing and generating longer sequences of text.
Training Details
The model was trained using SFT, a method that involves fine-tuning a pre-trained language model on a dataset of input-output pairs. This process aims to align the model's outputs with desired human-like responses. The training utilized specific versions of popular machine learning frameworks:
- TRL: 0.25.1
- Transformers: 4.57.3
- Pytorch: 2.9.1
- Datasets: 3.6.0
- Tokenizers: 0.22.2
Good For
- Interactive Applications: Ideal for chatbots, virtual assistants, and other applications requiring dynamic text responses.
- Content Creation: Can be used for generating creative content, answering questions, or expanding on given topics.
- Research and Development: Provides a fine-tuned base for further experimentation and adaptation to specific downstream tasks.