SciPeerAI-7B

SciPeerAI-7B

World's first LLM fine-tuned specifically for scientific fraud detection.

Built by Sameer Nadeem — BS Data Science student, Bahawalpur, Pakistan.

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What This Model Does

SciPeerAI-7B analyzes scientific papers and outputs a structured JSON report covering 14 fraud dimensions simultaneously — something no other model does.

Given a paper's title, authors, abstract, and text, it returns:

• Overall fraud confidence score (0.0 to 1.0)
• Risk score across 14 modules
• Fraud type classification (20 fraud taxonomy types)
• Final verdict: FRAUD DETECTED or CLEAN PAPER

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Model Details

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Performance

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Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

base = "mistralai/Mistral-7B-Instruct-v0.3"
adapter = "Abu-Sameer-66/SciPeerAI-7B"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.float16, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)

prompt = """<s>[INST] Analyze this scientific paper for fraud and integrity issues:

Title: Your Paper Title
Authors: Author Names
Year: 2024
Journal: Journal Name
Abstract: Your abstract text here...

Provide a detailed JSON analysis with fraud scores across all 14 dimensions. [/INST]
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=300, do_sample=False)
print(tokenizer.decode(output[0], skip_special_tokens=True))

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14 Fraud Detection Dimensions

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Part of SciPeerAI System

• 🌐 Live Web UI: scipeerai-ui.vercel.app
• ⚡ Live API: web-production-f526d.up.railway.app
• 📊 Benchmark Dataset: Abu-Sameer-66/SciPeerBench
• 💻 GitHub Backend: github.com/Abu-Sameer-66/SciPeerAI

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Citation

@misc{nadeem2026scipeerai,
  title={SciPeerAI: Multi-dimensional Automated Scientific Integrity Analysis System},
  author={Sameer Nadeem},
  year={2026},
  url={https://huggingface.co/Abu-Sameer-66/SciPeerAI-7B}
}