abcorrea/struct-v1
abcorrea/struct-v1 is a 4 billion parameter language model fine-tuned from Qwen/Qwen3-4B-Thinking-2507. This model was trained using Supervised Fine-Tuning (SFT) with the TRL framework. It is designed for general text generation tasks, leveraging its Qwen3 base for robust language understanding and generation capabilities.
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Model Overview
abcorrea/struct-v1 is a 4 billion parameter language model, fine-tuned from the Qwen/Qwen3-4B-Thinking-2507 base model. This model was developed by abcorrea and utilizes the TRL (Transformer Reinforcement Learning) library for its training procedure, specifically employing 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 to potentially enhance performance on specific tasks compared to its base model.
- Qwen3 Architecture: Leverages the robust architecture of the Qwen3 series for strong language understanding.
Training Details
The model was trained using the TRL framework (version 0.19.1) with Transformers (version 4.52.1), Pytorch (version 2.7.0), Datasets (version 4.0.0), and Tokenizers (version 0.21.1). The training method was Supervised Fine-Tuning (SFT).
Good For
- General text generation applications.
- Developers looking for a fine-tuned Qwen3-based model for various language tasks.
- Experimentation with SFT-trained models.