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ahammad115566
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Model Details
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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, BitsAndBytesConfigfrom peft import PeftModelMODEL_NAME = "ahammad115566/smeft-qwen-7b"RESPONSE_PREFIX = "\n### Response:\n"bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True,)model = AutoModelForCausalLM.from_pretrained( MODEL_NAME, quantization_config=bnb_config, device_map={"": 0}, trust_remote_code=True,)model.eval()tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token# ================= Inference ==============def build_prompt(instruction: str, context: str = "") -> str: parts = [ f"\n### Instruction:\n{instruction}"] parts.append(RESPONSE_PREFIX) return "\n".join(parts)@torch.inference_mode()def ask(instruction: str, context: str = "") -> str: inputs = tokenizer( build_prompt(instruction, context), return_tensors="pt", add_special_tokens=True, ).to("cuda:0") output_ids = model.generate( **inputs, max_new_tokens=512, 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()"""| 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 |
This model may occasionally:
May hallucinate operator identities.
Domain-locked by design. The model is not suitable for general-purpose tasks.
2,500 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
@misc{hammad2026smeftqwen, author = {Ahmed Hammad and Veronica Sanz}, title = {SMEFT-Qwen-7B: A Domain-Adapted Language Model for Standard Model Effective Field Theory}, year = {2026}, howpublished = {\url{https://huggingface.co/ahammad115566/smeft-qwen-7b}}}pip install torch transformers bitsandbytes accelerateimport torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigfrom peft import PeftModelMODEL_NAME = "ahammad115566/smeft-qwen-7b"RESPONSE_PREFIX = "\n### Response:\n"bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True,)model = AutoModelForCausalLM.from_pretrained( MODEL_NAME, quantization_config=bnb_config, device_map={"": 0}, trust_remote_code=True,)model.eval()tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token# ================= Inference ==============def build_prompt(instruction: str, context: str = "") -> str: parts = [ f"\n### Instruction:\n{instruction}"] parts.append(RESPONSE_PREFIX) return "\n".join(parts)@torch.inference_mode()def ask(instruction: str, context: str = "") -> str: inputs = tokenizer( build_prompt(instruction, context), return_tensors="pt", add_special_tokens=True, ).to("cuda:0") output_ids = model.generate( **inputs, max_new_tokens=512, 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()"""@misc{hammad2026smeftqwen, author = {Ahmed Hammad and Veronica Sanz}, title = {SMEFT-Qwen-7B: A Domain-Adapted Language Model for Standard Model Effective Field Theory}, year = {2026}, howpublished = {\url{https://huggingface.co/ahammad115566/smeft-qwen-7b}}}