Qwen2.5-0.5B-India-Propaganda-Detector

Qwen2.5-0.5B — India Anti-Propaganda Detector (QLoRA)

A QLoRA fine-tuned version of Qwen/Qwen2.5-0.5B, trained to detect anti-India propaganda in text.

🎯 Purpose

This model classifies text as either PROPAGANDA or NOT_PROPAGANDA and provides:

• Propaganda category identification
• Detailed reasoning for the classification

It distinguishes between genuine anti-India propaganda (misinformation, manipulated narratives) and legitimate criticism, balanced reporting, or academic analysis.

📋 Propaganda Categories Detected

🔧 Training Details

💻 Usage

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

# Load model
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)

base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-0.5B",
    quantization_config=bnb_config,
    device_map="auto",
)

model = PeftModel.from_pretrained(base_model, "ProfRutPatel/Qwen2.5-0.5B-India-Propaganda-Detector")
tokenizer = AutoTokenizer.from_pretrained("ProfRutPatel/Qwen2.5-0.5B-India-Propaganda-Detector")

# Analyze text
system_prompt = "You are an expert analyst specialized in detecting anti-India propaganda..."
text = "Your text to analyze here"

prompt = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\nAnalyze the following text for anti-India propaganda:\n\n\"{text}\"<|im_end|>\n<|im_start|>assistant\n"

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.1)
result = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(result)

⚠️ Limitations

• Trained on a curated dataset of ~86 examples; may not cover all propaganda patterns
• Best used as a screening tool, not a definitive classifier
• Should be combined with human review for critical applications
• May not perform well on very subtle or novel propaganda techniques

🏗️ Hardware

• Trained on NVIDIA GeForce GTX 1650 (4GB VRAM)
• QLoRA enables training on consumer GPUs