Hyeongwon/PS_bs256_Qwen3-4B-Base_0322-01
Hyeongwon/PS_bs256_Qwen3-4B-Base_0322-01 is a 4 billion parameter language model, fine-tuned from Hyeongwon/Qwen3-4B-Base using the TRL framework. This model, with a 32768 token context length, has undergone Supervised Fine-Tuning (SFT) to enhance its conversational capabilities. It is designed for general text generation tasks, particularly excelling in responding to open-ended prompts and user queries.
Loading preview...
Model Overview
Hyeongwon/PS_bs256_Qwen3-4B-Base_0322-01 is a 4 billion parameter language model, building upon the base architecture of Hyeongwon/Qwen3-4B-Base. This model has been specifically fine-tuned using the TRL (Transformer Reinforcement Learning) framework, leveraging Supervised Fine-Tuning (SFT) techniques.
Key Capabilities
- General Text Generation: Capable of generating coherent and contextually relevant text based on user prompts.
- Conversational AI: Optimized through SFT for engaging in question-answering and open-ended dialogue.
- Base Model Enhancement: Represents a fine-tuned iteration of the Qwen3-4B-Base, suggesting improved performance in specific tasks compared to its base counterpart.
Training Details
The model's training procedure involved Supervised Fine-Tuning (SFT) using the TRL library (version 0.25.1). Other framework versions used include Transformers 4.57.3, Pytorch 2.6.0, Datasets 3.6.0, and Tokenizers 0.22.2. Further details on the training run can be explored via the Weights & Biases project.
When to Use This Model
This model is suitable for applications requiring a 4 billion parameter model with a 32768 token context length that has been specifically fine-tuned for improved conversational and text generation performance. It can be a good choice for tasks such as chatbots, content creation, or interactive AI systems where a refined response quality is desired.