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Model: ahammad115566/qwen-smeft
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- large-language-model
+- qwen3
+qwen:
+- physics
+smeft:
+- effective-field-theory
+high-energy-physics:
+- scientific-llm
+pipeline_tag: text-generation
+library_name: transformers
+base_model: Qwen/Qwen3-8B
+model_type: qwen3
+do_sample: true
+temperature: 0.1
+top_p: 0.1
+repetition_penalty: 1.1
+max_new_tokens: 512
+---
+
+<p align="center">
+<img src="logo.png" width="350">
+</p>
+
+<p align="center">
+
+A domain-adapted large language model for 
+<b>Standard Model Effective Field Theory (SMEFT)</b>.
+
+</p>
+
+# 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
+
+```bash
+pip install torch transformers bitsandbytes accelerate
+```
+## Inference Example
+
+```python
+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:
+```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"
+}
+```