Hyeongwon/P2-split2_prob_Qwen3-14B-Base_0405_1e-5
The Hyeongwon/P2-split2_prob_Qwen3-14B-Base_0405_1e-5 model is a 14 billion parameter language model, fine-tuned from Qwen/Qwen3-14B-Base. This model was trained using Supervised Fine-Tuning (SFT) with the TRL framework. It is designed for general text generation tasks, leveraging its base Qwen3 architecture and 32768 token context length. The fine-tuning process aims to enhance its performance for specific probabilistic text generation applications.
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Model Overview
This model, Hyeongwon/P2-split2_prob_Qwen3-14B-Base_0405_1e-5, is a 14 billion parameter language model derived from the robust Qwen/Qwen3-14B-Base architecture. It has undergone Supervised Fine-Tuning (SFT) using the TRL library, indicating a focus on adapting its base capabilities to specific tasks or data distributions.
Key Characteristics
- Base Model: Fine-tuned from Qwen/Qwen3-14B-Base, inheriting its foundational language understanding and generation capabilities.
- Training Method: Utilizes Supervised Fine-Tuning (SFT) for specialized performance.
- Framework: Developed with the TRL (Transformer Reinforcement Learning) library, version 0.25.1.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing and generating longer sequences of text.
Potential Use Cases
Given its fine-tuned nature and base model, this model is suitable for:
- General text generation: Creating coherent and contextually relevant text based on prompts.
- Probabilistic text generation tasks: Applications where the fine-tuning has optimized for specific probabilistic outcomes in text.
- Further research and development: Serving as a strong base for additional fine-tuning or experimentation in language model applications.