PetarKal/Qwen3-4B-ascii-art-curated-mix-v4-full-lr2e-5-ga16-ctx4096
PetarKal/Qwen3-4B-ascii-art-curated-mix-v4-full-lr2e-5-ga16-ctx4096 is a 4 billion parameter language model fine-tuned from Qwen/Qwen3-4B-Base. This model was trained using SFT with TRL, focusing on specific data curation to enhance its capabilities. It is designed for text generation tasks, leveraging its base architecture and fine-tuning for specialized outputs.
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Overview
This model, PetarKal/Qwen3-4B-ascii-art-curated-mix-v4-full-lr2e-5-ga16-ctx4096, is a 4 billion parameter language model built upon the Qwen3-4B-Base architecture. It has undergone specific fine-tuning using the TRL (Transformers Reinforcement Learning) library, indicating a focus on optimizing its performance for particular tasks through supervised fine-tuning (SFT).
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on given prompts.
- Fine-tuned Performance: Benefits from SFT training, suggesting enhanced performance in specific domains or styles compared to its base model.
- Qwen3 Architecture: Leverages the robust capabilities of the Qwen3-4B-Base model.
Training Details
The model was trained using SFT (Supervised Fine-Tuning), a common method for adapting pre-trained language models to specific tasks or datasets. The training utilized the TRL framework, which is designed for transformer-based models. The development environment included TRL 0.29.0, Transformers 5.3.0, Pytorch 2.10.0, Datasets 4.7.0, and Tokenizers 0.22.2.
Use Cases
This model is suitable for various text generation applications where a fine-tuned Qwen3-4B variant is beneficial. Its specific fine-tuning implies potential strengths in areas related to its curated training data, though the exact nature of this curation is not detailed in the provided README.