README.md

---
datasets:
- theeseus-ai/RiskClassifier
base_model:
- meta-llama/Llama-3.1-8B-Instruct
---


# RiskClassifier: Fine-Tuned LLaMA 3.1 8B Model

## Model Summary
**RiskClassifier** is a fine-tuned version of the **meta-llama/Llama-3.1-8B-Instruct** model, designed to evaluate risk levels across diverse scenarios using structured critical thinking. It is fine-tuned on the **theeseus-ai/RiskClassifier** dataset, which focuses on assessing and labeling risk scores while maintaining detailed reasoning explanations. This model is optimized for tasks requiring risk classification, fraud detection, and analytical reasoning.

## Model Details
- **Base Model**: [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)
- **Fine-tuned Dataset**: [theeseus-ai/RiskClassifier](https://huggingface.co/datasets/theeseus-ai/RiskClassifier)
- **Model Size**: 8 Billion Parameters
- **Language**: English
- **License**: Apache 2.0
- **Use Case**: Risk assessment, fraud detection, critical thinking tasks

## Dataset Information
The **RiskClassifier** dataset provides structured scenarios with:
- **Context**: A description of the event requiring analysis.
- **Query**: A critical-thinking question tied to the scenario.
- **Answers**: Four risk level options ("Low risk," "Moderate risk," "High risk," "Very high risk").
- **Risk Score**: A numeric value (0–100) representing the raw risk assessment.
- **Conversations**: Reformatted data in ShareGPT-style conversation format to train the model for reasoning and structured responses.

Example Reformatted Output:
```
{
  "context": "A customer used a credit card in a high-fraud region for a large purchase.",
  "query": "What is the risk level of this transaction?",
  "answers": ["Low risk", "Moderate risk", "High risk", "Very high risk"],
  "risk_score": 85,
  "conversations": [
    {"role": "system", "content": "You are a helpful AI that assesses risk levels and provides explanations."},
    {"role": "user", "content": "Context: A customer used a credit card in a high-fraud region for a large purchase.\nQuestion: What is the risk level of this transaction?\nAnswers: [Low risk, Moderate risk, High risk, Very high risk]"},
    {"role": "assistant", "content": "Risk Level: Very high risk (Score: 85)"}
  ]
}
```

## Intended Use
### Applications
- **Fraud Detection**: Evaluating suspicious transactions and identifying high-risk activities.
- **Risk Analysis**: Assessing scenarios with probabilistic evaluations for financial and operational decisions.
- **Critical Thinking Tasks**: Enhancing AI's ability to reason about uncertainty and complex situations.
- **Educational Tools**: Training AI systems to provide explanations for risk assessments.

### Limitations
- **Context Dependency**: Accuracy may degrade with ambiguous or incomplete context.
- **Bias Risk**: Outputs may inherit biases present in training data; manual review is advised for high-impact decisions.
- **Numeric Risk Scores**: The numerical scores may require post-processing to fit domain-specific thresholds.

## How to Use
### Example Code:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "theeseus-ai/RiskClassifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

inputs = tokenizer("Context: A large transaction flagged for manual review.\nQuestion: What is the risk level?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0]))
```

## Evaluation Metrics
- **Accuracy**: Verified predictions against labeled risk levels.
- **Reasoning Completeness**: Evaluated explanations for clarity and alignment with context.
- **Risk Score Consistency**: Checked correlation between numeric risk scores and label predictions.

## Training Configuration
- **Optimizer**: AdamW
- **Batch Size**: 32
- **Learning Rate**: 2e-5
- **Epochs**: 3
- **Hardware**: NVIDIA A100 GPUs
- **Precision**: bf16 mixed precision

## Environmental Impact
- **Hardware**: NVIDIA A100 GPUs
- **Training Hours**: ~2 hours
- **Carbon Emissions**: Estimated using [ML CO2 Calculator](https://mlco2.github.io/impact)

## Citation
```
@misc{RiskClassifier2024,
  title={RiskClassifier: Fine-Tuned LLaMA 3.1 8B Model for Risk Assessment},
  author={Theeseus AI},
  year={2024},
  howpublished={\url{https://huggingface.co/theeseus-ai/RiskClassifier}}
}
```

## Contact
For inquiries, please reach out to **[email protected]** or visit [LinkedIn](https://www.linkedin.com/in/theeseus).