Hyeongwon/P2-split1_prob_Qwen3-8B-Base_0325-01
Hyeongwon/P2-split1_prob_Qwen3-8B-Base_0325-01 is an 8 billion parameter language model, fine-tuned from ChuGyouk/Qwen3-8B-Base using the TRL framework. This model was trained with Supervised Fine-Tuning (SFT) and features a context length of 32768 tokens. It is designed for general text generation tasks, building upon the capabilities of its base Qwen3-8B architecture.
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Model Overview
Hyeongwon/P2-split1_prob_Qwen3-8B-Base_0325-01 is an 8 billion parameter language model, derived from the ChuGyouk/Qwen3-8B-Base architecture. This model has undergone Supervised Fine-Tuning (SFT) utilizing the TRL (Transformer Reinforcement Learning) framework, indicating a focus on refining its response generation capabilities.
Key Characteristics
- Base Model: Fine-tuned from ChuGyouk/Qwen3-8B-Base.
- Training Method: Trained using Supervised Fine-Tuning (SFT) with the TRL library.
- Parameter Count: Features 8 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, enabling it to process and generate longer, more coherent texts.
Intended Use Cases
This model is suitable for a variety of text generation tasks, leveraging its SFT training to produce relevant and contextually appropriate outputs. Developers can integrate it into applications requiring conversational AI, content creation, or general question-answering, particularly where the base Qwen3-8B's strengths are beneficial. The provided quick start guide demonstrates its use for text generation with a simple Python pipeline.