Hyeongwon/P2_prob_Qwen3-4B-Base_0311-01
Hyeongwon/P2_prob_Qwen3-4B-Base_0311-01 is a 4 billion parameter language model developed by Hyeongwon, fine-tuned from Hyeongwon/Qwen3-4B-Base. This model, trained using the TRL framework, is designed for text generation tasks with a context length of 32768 tokens. Its primary application is generating responses to user prompts, demonstrating capabilities in conversational AI.
Loading preview...
Model Overview
Hyeongwon/P2_prob_Qwen3-4B-Base_0311-01 is a 4 billion parameter language model, fine-tuned by Hyeongwon from its base model, Hyeongwon/Qwen3-4B-Base. This model leverages the Transformer Reinforcement Learning (TRL) framework for its training, specifically utilizing Supervised Fine-Tuning (SFT).
Key Capabilities
- Text Generation: Optimized for generating coherent and contextually relevant text based on user prompts.
- Conversational AI: Demonstrates proficiency in responding to open-ended questions, as shown in the quick start example.
- TRL Framework: Built upon the TRL library, indicating potential for further reinforcement learning applications.
Training Details
The model was trained using SFT, a common method for adapting pre-trained language models to specific tasks. The training process utilized several key frameworks:
- TRL: 0.25.1
- Transformers: 4.57.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.22.2
Good For
- General Text Generation: Suitable for various applications requiring natural language output.
- Interactive Applications: Can be integrated into chatbots or interactive systems to generate dynamic responses.
- Further Fine-tuning: As a fine-tuned model itself, it can serve as a strong base for additional task-specific adaptations.