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README.md

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 ---
 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
+# Model Card for Model ID
 
-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.
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
 
-Fine-tuned model can be tested on Google Colab using Nvidia A100 or L4 GPU.
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
 
-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
+## Citation [optional]
 
-#Login to Huggingface to load Mistral LLM
-login("Huggingface access token")
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
-model_id = "mistralai/Mistral-7B-Instruct-v0.1"
-peft_model_name="bpavlsh/Mistral-Fake-News-Detection"
+**BibTeX:**
 
-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)
+[More Information Needed]
 
-text=""" News text for analysis, from 1Kb to 10Kb """
+**APA:**
 
-prompt = f"""<s>[INST] <<SYS>>
-You are an expert in analyzing news for fake content, propaganda, and offensive language.
-<</SYS>> 
+[More Information Needed]
 
-Please analyze the following text: {text} [/INST]"""
+## Glossary [optional]
 
-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}")
-```
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
-## References
+[More Information Needed]
 
-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
+## More Information [optional]
 
-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
+[More Information Needed]
 
-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
+## Model Card Authors [optional]
 
-## Disclaimer
+[More Information Needed]
 
-We are sharing a considered model and results for academic purpose only, 
-not any  advice or recommendations.
+## Model Card Contact
 
-## Contacts
+[More Information Needed]
+### Framework versions
 
-B. Pavlyshenko https://www.linkedin.com/in/bpavlyshenko
+- PEFT 0.15.2

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