Hyeongwon/P2-split2_prob_Qwen3-8B-Base_0325-06-bs256-epoch10
Hyeongwon/P2-split2_prob_Qwen3-8B-Base_0325-06-bs256-epoch10 is an 8 billion parameter causal language model, fine-tuned from ChuGyouk/Qwen3-8B-Base using Supervised Fine-Tuning (SFT) with a 32K context length. This model is optimized for general text generation tasks, building upon the Qwen3 architecture. It is suitable for applications requiring robust language understanding and generation capabilities.
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Model Overview
This model, Hyeongwon/P2-split2_prob_Qwen3-8B-Base_0325-06-bs256-epoch10, is an 8 billion parameter language model fine-tuned from the ChuGyouk/Qwen3-8B-Base architecture. It leverages a substantial context length of 32,768 tokens, making it capable of processing and generating longer sequences of text.
Key Capabilities
- Base Model Fine-tuning: Built upon the Qwen3-8B-Base, indicating a strong foundation in general language understanding.
- Supervised Fine-Tuning (SFT): The model has undergone SFT, suggesting it has been trained on specific datasets to improve performance on particular tasks, though the exact nature of these tasks is not detailed in the README.
- Extended Context Window: With a 32K token context length, it can handle complex prompts and generate coherent, contextually relevant responses over longer interactions.
Training Details
The model was trained using the TRL (Transformer Reinforcement Learning) library, specifically employing Supervised Fine-Tuning. The training process utilized TRL 0.25.1, Transformers 4.57.3, Pytorch 2.6.0, Datasets 3.6.0, and Tokenizers 0.22.2.
Use Cases
This model is suitable for a variety of text generation tasks where a robust base model with a large context window is beneficial. Its fine-tuned nature implies improved performance over the base model for general conversational AI, content creation, and question-answering systems, particularly those requiring an understanding of extensive input.