ahammad115566/smeft-qwen-7b
SMEFT-Qwen-7B is a 7.6 billion parameter large language model developed by Ahmed Hammad and Veronica Sanz, fine-tuned from Qwen2.5-7B-Instruct. This model is specifically domain-adapted for research assistance in Standard Model Effective Field Theory (SMEFT) and related high-energy physics frameworks. It excels at SMEFT operator reasoning, EFT basis translation, and physics-aware scientific dialogue, making it ideal for structured theoretical question answering in particle physics.
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SMEFT-Qwen-7B: Domain-Adapted LLM for High-Energy Physics
SMEFT-Qwen-7B is a 7.6 billion parameter large language model, fine-tuned from Qwen2.5-7B-Instruct, specifically designed for research in Standard Model Effective Field Theory (SMEFT) and other effective field theory frameworks within high-energy physics. Developed by Ahmed Hammad and Veronica Sanz, this model leverages a curated corpus of SMEFT and particle physics literature to provide specialized assistance.
Key Capabilities
- SMEFT operator reasoning: Understands and processes complex SMEFT operators.
- EFT basis translation: Facilitates translation between different effective field theory bases.
- Physics-aware scientific dialogue: Engages in technical discussions with an understanding of physics principles.
- Literature-style technical explanation: Generates explanations consistent with scientific literature.
- Structured theoretical question answering: Provides answers to specific theoretical physics questions.
Training and Limitations
The model was fine-tuned using LoRA on a specialized dataset of SMEFT and high-energy physics preprints. While optimized for its domain, users should be aware of potential limitations, including occasional hallucination of operator identities, incorrect numerical coefficients, or confusion regarding basis conventions. All outputs should be independently verified against primary literature.