omaragha/sports_assistant_merged
The omaragha/sports_assistant_merged is a 2 billion parameter Qwen3 model, fine-tuned by omaragha, optimized for sports-related assistance. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. With a 32768 token context length, it is designed for efficient processing of sports-specific queries and information.
Loading preview...
Model Overview
The omaragha/sports_assistant_merged is a 2 billion parameter Qwen3 model, developed by omaragha. It has been fine-tuned from the unsloth/qwen3-1.7b-unsloth-bnb-4bit base model, leveraging the Unsloth library and Huggingface's TRL for accelerated training.
Key Characteristics
- Architecture: Qwen3, a causal language model.
- Parameter Count: 2 billion parameters, offering a balance between performance and efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, enabling processing of longer inputs.
- Training Efficiency: Achieved 2x faster training due to the integration of Unsloth, making it a highly optimized fine-tuned model.
Intended Use Cases
This model is particularly well-suited for applications requiring a compact yet capable language model with a focus on sports-related content. Its efficient training process suggests potential for rapid deployment and iteration in specialized domains. Developers can utilize this model for tasks such as generating sports news summaries, answering sports trivia, or assisting with sports data analysis, especially where the Qwen3 architecture and Unsloth's training optimizations are beneficial.