ahammad115566/qwen-smeft
The ahammad115566/qwen-smeft model is a domain-adapted large language model fine-tuned from Qwen3-8B by Ahmed Hammad and Veronica Sanz. It is specifically optimized for tasks related to Standard Model Effective Field Theory (SMEFT), including operator reasoning, EFT basis translation, and physics-aware scientific dialogue. This model excels at structured theoretical question answering within the particle physics domain, leveraging a curated corpus of SMEFT and high-energy physics literature.
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Overview
This model, developed by Ahmed Hammad and Veronica Sanz, is a domain-adapted large language model specifically fine-tuned for Standard Model Effective Field Theory (SMEFT). It is built upon the Qwen3-8B base model and was fine-tuned using LoRA with a curated corpus of SMEFT and high-energy physics preprints. The training focused on instruction-following scientific question answering within this specialized domain.
Key Capabilities
- SMEFT operator reasoning: Understands and processes complex SMEFT operators.
- EFT basis translation: Capable of translating between different Effective Field Theory bases.
- Physics-aware scientific dialogue: Engages in technical discussions grounded in particle physics principles.
- Literature-style technical explanation: Generates explanations in a style consistent with scientific literature.
- Structured theoretical question answering: Provides structured answers to theoretical physics questions.
Limitations and Considerations
- Domain-locked: This model is intentionally designed for SMEFT and is not suitable for general-purpose tasks.
- Potential for hallucination: May occasionally hallucinate operator identities.
- Uneven coverage: Due to 3620 training examples, coverage of the SMEFT operator space might be uneven, potentially leading to less reliable answers for rare operators or non-Warsaw bases.
Usage Note
When using Qwen3's native enable_thinking toggle, set it to False to avoid conflicts with this model's custom structured prompt format for reasoning.