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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 acceleratepython
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig MODEL_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"}pip install torch transformers bitsandbytes accelerateimport torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig MODEL_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)@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"}