ahammad115566/qwen-smeft

A domain-adapted large language model for Standard Model Effective Field Theory (SMEFT).

Capabilities

The model has been optimized for:

• SMEFT operator reasoning

• EFT basis translation

• Physics-aware scientific dialogue

• Literature-style technical explanation

• Structured theoretical question answering

This model is fine-tuned from Qwen3-8B using a curated corpus of SMEFT and particle physics literature.

Note on Qwen3 thinking mode: Qwen3 supports a native enable_thinking toggle in its chat template that wraps reasoning in <think>...</think> blocks. This model was tuned to produce reasoning through its own structured prompt format (not Qwen3's native thinking blocks), so if you build prompts via tokenizer.apply_chat_template(...), set enable_thinking=False to avoid mixing the two reasoning styles. The example below uses a raw instruction-style prompt and is unaffected either way.

Quick Start

Installation

pip install torch transformers bitsandbytes accelerate

Inference Example

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

MODEL_NAME = "ahammad115566/qwen-smeft"
RESPONSE_PREFIX = "\n### Response:\n"

# 4-bit quantisation
bnb_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 tokenizer
tokenizer = 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()


# Example
instruction = """
Which SMEFT operators modify EWPO?
"""

response = ask(instruction)
print(response)

---

Training Details

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

---

About the model

• 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.

---

Authors

Ahmed Hammad
Assistant professor

Center of AI and natural science, KIAS, Seoul.

Veronica Sanz
Professor of Theoretical Physics

University of Valencia

Citation

A technical paper describing the dataset construction and fine-tuning procedure is forthcoming.

Please cite the model as:

@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"
}