---
base_model: mistralai/Mistral-7B-Instruct-v0.1
library_name: peft
tags:
  - fake news detection
  - propaganda
  - fake news
  - propaganda detection
  - manipulative constructions analysis
  - offensive language analysis
  - peft
  - text-generation
  - summarization
license: apache-2.0
---

 ## Model Description

 Fine-tuned Mistral-7B model for detecting and analyzing fake news, propaganda and offensive language in news articles (English language).
It was fine-tuned using Peft/LoRA approach with 4-Bit quantization. Given the news text, the model detects and analyses fake news  and propaganda, analyses 
and shows manipulative constructions in the text as well as shows offensive language.

 ## How to Get Started with the Model

 Fine-tuned model can be tested on Google Colab using Nvidia A100 or L4 GPU.

Pakages installation:
```python
pip install transformers bitsandbytes peft
```
Use the code below to get started with the model:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
from huggingface_hub import login

#Login to Huggingface to load Mistral LLM
login("Huggingface access token")

model_id = "mistralai/Mistral-7B-Instruct-v0.1"
peft_model_name="bpavlsh/Mistral-Fake-News-Detection"

tokenizer = AutoTokenizer.from_pretrained(model_id)
base_model = AutoModelForCausalLM.from_pretrained( model_id, load_in_4bit=True,
                                  device_map="auto", torch_dtype="auto")
model = PeftModel.from_pretrained(base_model, peft_model_name)

text=""" News text for analysis, from 1Kb to 10Kb """

prompt = f"""<s>[INST] <<SYS>>
You are an expert in analyzing news for fake content, propaganda, and offensive language.
<</SYS>> 

Please analyze the following text: {text} [/INST]"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=1500)
output_result=tokenizer.decode(output[0], skip_special_tokens=True)
result=output_result.split('[/INST]')[1]
print(f"\n{result}")
```

 ## References

 Pavlyshenko B.M. Analysis of Disinformation and Fake News Detection Using Fine-Tuned Large Language Model. arXiv preprint arXiv:2309.04704. 2023. Download PDF: https://arxiv.org/pdf/2309.04704.pdf

 Pavlyshenko B.M. Financial News Analytics Using Fine-Tuned Llama 2 GPT Model. arXiv preprint arXiv:2308.13032. 2023. Download PDF: https://arxiv.org/pdf/2308.13032.pdf

 Pavlyshenko B.M. AI Approaches to Qualitative and Quantitative News Analytics on NATO Unity. arXiv preprint arXiv:2505.06313. 2025. Download PDF: https://arxiv.org/pdf/2505.06313

 ## Disclaimer

 We are sharing a considered model and results for academic purpose only, 
not any  advice or recommendations.

 ## Contacts

 B. Pavlyshenko https://www.linkedin.com/in/bpavlyshenko