ahammad115566/smeft-qwen-7b
SMEFT-Qwen-7B is a 7 billion parameter Qwen2.5-based large language model developed by Ahmed Hammad and Veronica Sanz, specifically fine-tuned for research assistance in Standard Model Effective Field Theory (SMEFT) and related high-energy physics frameworks. This domain-adapted model excels at SMEFT operator reasoning, EFT basis translation, and physics-aware scientific dialogue. It is optimized for structured theoretical question answering within the high-energy physics domain.
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SMEFT-Qwen-7B Overview
SMEFT-Qwen-7B is a specialized large language model, fine-tuned from Qwen2.5-7B-Instruct, designed for applications in Standard Model Effective Field Theory (SMEFT) and related effective field theories in high-energy physics. Developed by Ahmed Hammad and Veronica Sanz, this model leverages a curated corpus of SMEFT and particle physics literature to provide domain-specific research 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 in a style consistent with scientific literature.
- Structured theoretical question answering: Provides answers to theoretical questions within the SMEFT domain.
Limitations and Considerations
While highly specialized, the model has limitations including potential hallucinations of operator identities and an uneven coverage of the SMEFT operator space due to its 1,700 training examples. It is domain-locked by design and not suitable for general-purpose tasks. Users should independently verify outputs against primary literature.