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ahammad115566
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Model Details
Model Provider
ahammad115566
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
bash
pip install torch transformers bitsandbytes accelerate
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigMODEL_NAME = "ahammad115566/qwen-smeft"RESPONSE_PREFIX = "\n### Response:\n"# 4-bit quantisationbnb_config = BitsAndBytesConfig(load_in_4bit=True,bnb_4bit_quant_type="nf4",bnb_4bit_compute_dtype=torch.bfloat16,bnb_4bit_use_double_quant=True,)# Load model and tokenizertokenizer = AutoTokenizer.from_pretrained(MODEL_NAME,trust_remote_code=True,)model = AutoModelForCausalLM.from_pretrained(MODEL_NAME,quantization_config=bnb_config,device_map={"": 0},trust_remote_code=True,)model.eval()if tokenizer.pad_token is None:tokenizer.pad_token = tokenizer.eos_token# ================= Inference ================def build_prompt(instruction: str) -> str:"""Construct the prompt using the format used during fine-tuning."""return f"\n### Instruction:\n{instruction}\n{RESPONSE_PREFIX}"@torch.inference_mode()def ask(instruction: str) -> str:"""Generate a response to an SMEFT-related instruction."""prompt = build_prompt(instruction)inputs = tokenizer(prompt,return_tensors="pt",add_special_tokens=True,).to(model.device)output_ids = model.generate(**inputs,max_new_tokens=2048,do_sample=False,repetition_penalty=1.1,eos_token_id=tokenizer.eos_token_id,pad_token_id=tokenizer.pad_token_id,)new_tokens = output_ids[0][inputs["input_ids"].shape[-1]:]return tokenizer.decode(new_tokens,skip_special_tokens=True,).strip()# Exampleinstruction = """Which SMEFT operators modify EWPO?"""response = ask(instruction)print(response)
| Parameter | Value |
|---|---|
| Base Model | Qwen3 |
| Fine-tuning Method | LoRA (merged) |
| Inference Quantization | 4-bit NF4 (bitsandbytes) |
| Domain | Standard Model Effective Field Theory (SMEFT) |
| Training Corpus | Curated SMEFT and HEP preprints |
| Task Format | Instruction-following scientific QA |
May hallucinate operator identities.
Domain-locked by design. The model is not suitable for general-purpose tasks.
3620 training examples. Coverage of the SMEFT operator space may be uneven; rare operators or non-Warsaw bases may be answered less reliably.
20% out of domain exmaples are included during the training.
Ahmed Hammad
Assistant professor
Center of AI and natural science, KIAS, Seoul.
Veronica Sanz
Professor of Theoretical Physics
University of Valencia
A technical paper describing the dataset construction and fine-tuning procedure is forthcoming.
Please cite the model as:
bibtex
@article{Hammad:2026bvw,author = "Hammad, Ahmed and Sanz, Veronica",title = "{Language-Guided Hypotheses Generation for Sparse SMEFT Analyses}",eprint = "2608.04100",archivePrefix = "arXiv",primaryClass = "hep-ph",month = "8",year = "2026"}