Hyeongwon/P2-split4_prob_Qwen3-1.7B-Base_0325-01
Hyeongwon/P2-split4_prob_Qwen3-1.7B-Base_0325-01 is a 2 billion parameter language model, fine-tuned from Hyeongwon/Qwen3-1.7B-Base. This model was trained using Supervised Fine-Tuning (SFT) with the TRL framework, building upon a base Qwen3 architecture. It is designed for general text generation tasks, leveraging its 32768 token context length for processing longer inputs. The model's fine-tuning process aims to enhance its probabilistic text generation capabilities.
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Model Overview
Hyeongwon/P2-split4_prob_Qwen3-1.7B-Base_0325-01 is a 2 billion parameter language model, derived from the Hyeongwon/Qwen3-1.7B-Base architecture. This model has undergone Supervised Fine-Tuning (SFT) using the TRL library, a framework for Transformer Reinforcement Learning. The training process focused on enhancing its probabilistic text generation, making it suitable for various generative AI applications.
Key Characteristics
- Base Model: Fine-tuned from Hyeongwon/Qwen3-1.7B-Base.
- Parameter Count: Features approximately 2 billion parameters.
- Context Length: Supports a substantial context window of 32768 tokens.
- Training Method: Utilizes Supervised Fine-Tuning (SFT) for specialized performance.
- Frameworks: Developed with TRL (version 0.25.1), Transformers (version 4.57.3), Pytorch (version 2.6.0), Datasets (version 3.6.0), and Tokenizers (version 0.22.2).
Intended Use Cases
This model is primarily designed for text generation tasks where a fine-tuned Qwen3-based model with a large context window is beneficial. Its SFT training suggests improved performance on tasks aligned with its fine-tuning data, making it a candidate for applications requiring nuanced or context-aware text outputs.