Hyeongwon/P9-split3_prob_Qwen3-4B-Base_0322-01
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:Mar 21, 2026Architecture:Transformer Warm
Hyeongwon/P9-split3_prob_Qwen3-4B-Base_0322-01 is a 4 billion parameter causal language model developed by Hyeongwon, fine-tuned from the Qwen3-4B-Base architecture. This model was trained using Supervised Fine-Tuning (SFT) with the TRL framework, offering a 32768 token context length. It is designed for text generation tasks, building upon its base model's capabilities.
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Model Overview
Hyeongwon/P9-split3_prob_Qwen3-4B-Base_0322-01 is a 4 billion parameter language model, fine-tuned by Hyeongwon from the existing Qwen3-4B-Base architecture. This model leverages a substantial 32768 token context window, making it suitable for processing longer sequences of text.
Key Capabilities
- Text Generation: The model is primarily designed for text generation tasks, as demonstrated by its quick start example for answering open-ended questions.
- Fine-tuned Performance: It has undergone Supervised Fine-Tuning (SFT) using the TRL library, indicating an optimization for specific downstream applications or improved instruction following.
- Framework Utilization: Developed with TRL (version 0.25.1), Transformers (4.57.3), Pytorch (2.6.0), Datasets (3.6.0), and Tokenizers (0.22.2), ensuring compatibility with modern deep learning ecosystems.
Good For
- General Text Generation: Suitable for various text generation applications where a 4 billion parameter model with a large context window is beneficial.
- Further Research and Development: As a fine-tuned base model, it provides a strong foundation for further experimentation or adaptation to more specialized tasks.