Hyeongwon/P9-split5_prob_Qwen3-4B-Base_0322-01
Hyeongwon/P9-split5_prob_Qwen3-4B-Base_0322-01 is a 4 billion parameter causal language model developed by Hyeongwon, fine-tuned from Hyeongwon/Qwen3-4B-Base using TRL. This model is designed for text generation tasks, leveraging its base architecture and fine-tuning for improved performance. It offers a 32768 token context length, making it suitable for applications requiring substantial input understanding.
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Model Overview
Hyeongwon/P9-split5_prob_Qwen3-4B-Base_0322-01 is a 4 billion parameter language model developed by Hyeongwon. It is a fine-tuned variant of the Hyeongwon/Qwen3-4B-Base model, specifically trained using the Transformer Reinforcement Learning (TRL) library. This model is designed for general text generation tasks, building upon the capabilities of its base model.
Key Capabilities
- Text Generation: Optimized for generating coherent and contextually relevant text based on given prompts.
- Fine-tuned Performance: Benefits from Supervised Fine-Tuning (SFT) using TRL, which enhances its ability to follow instructions and produce desired outputs.
- Large Context Window: Supports a context length of 32768 tokens, allowing it to process and generate longer sequences of text while maintaining context.
Training Details
The model underwent a Supervised Fine-Tuning (SFT) process. The training utilized several standard machine learning frameworks, including 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). Further details on the training run can be found on Weights & Biases.
Good For
This model is suitable for developers and researchers looking for a 4 billion parameter model with a substantial context window for various text generation applications, particularly those that can benefit from a model fine-tuned with TRL.