Hyeongwon/P2-split2_prob_Qwen3-1.7B-Base_0325-01
Hyeongwon/P2-split2_prob_Qwen3-1.7B-Base_0325-01 is a 2 billion parameter language model developed by Hyeongwon, fine-tuned from the Qwen3-1.7B-Base architecture. This model was trained using Supervised Fine-Tuning (SFT) with the TRL framework, focusing on specific conversational or generative tasks. It is designed for text generation applications, offering a 32768 token context length for processing longer inputs.
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Model Overview
Hyeongwon/P2-split2_prob_Qwen3-1.7B-Base_0325-01 is a 2 billion parameter language model, fine-tuned by Hyeongwon from the base Qwen3-1.7B architecture. This model leverages a 32768 token context length, making it suitable for tasks requiring extensive input understanding or generation.
Training Details
The model underwent Supervised Fine-Tuning (SFT) using the TRL framework (version 0.25.1). The training process utilized Transformers (4.57.3), Pytorch (2.6.0), Datasets (3.6.0), and Tokenizers (0.22.2).
Key Capabilities
- Text Generation: Optimized for generating coherent and contextually relevant text based on user prompts.
- Conversational AI: Demonstrated through its quick start example, it can engage in open-ended question answering.
Good For
- Prototyping and Development: A suitable base for further fine-tuning on specific downstream tasks.
- Exploratory Text Generation: Ideal for experimenting with different prompts and generating creative or informative responses.
- Applications requiring moderate context: Its 32768 token context window supports more complex interactions than smaller models.