abcorrea/struct-v3
abcorrea/struct-v3 is a 4 billion parameter causal language model, fine-tuned from Qwen/Qwen3-4B-Thinking-2507, with a context length of 40960 tokens. Developed by abcorrea, this model is optimized for general text generation tasks, leveraging its base architecture for robust language understanding and generation capabilities. Its fine-tuning process aims to enhance its performance in conversational and question-answering scenarios.
Loading preview...
Model Overview
abcorrea/struct-v3 is a 4 billion parameter language model, fine-tuned from the Qwen/Qwen3-4B-Thinking-2507 base model. This model leverages a substantial 40960-token context window, enabling it to process and generate longer, more coherent texts. The fine-tuning process, conducted using the TRL library, focuses on enhancing its conversational and generative capabilities.
Key Capabilities
- General Text Generation: Capable of generating diverse and contextually relevant text based on given prompts.
- Extended Context Understanding: Benefits from a 40960-token context length, allowing for better comprehension and generation in longer interactions.
- Instruction Following: Designed to respond effectively to user instructions, making it suitable for various interactive applications.
Training Details
The model was trained using Supervised Fine-Tuning (SFT) with the TRL framework. The training environment utilized specific versions of key libraries:
- TRL: 0.19.1
- Transformers: 4.52.1
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.1
Good For
- Conversational AI: Its fine-tuned nature makes it suitable for chatbots and interactive agents.
- Content Creation: Generating creative or informative text for various applications.
- Question Answering: Providing detailed responses to complex queries, leveraging its large context window.