---
language: en
license: llama3.1
tags:
- llama
- transformer
- 8b
- 4bit
- instruction-tuning
- conversational
- llama3
- meta
pipeline_tag: text-generation
inference: true
model_creator: 0xroyce
model_type: LLaMA
datasets:
- 0xroyce/Plutus
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
---
# NOTE: MODEL IS BEING FINE-TUNED WITH DATEST LATEST

# Plutus-Meta-Llama-3.1-8B-Instruct-bnb-4bit

Plutus-Meta-Llama-3.1-8B-Instruct-bnb-4bit is a fine-tuned version of the LLaMA-3.1-8B model, specifically optimized for tasks related to finance, economics, trading, psychology, and social engineering. This model leverages the LLaMA architecture and employs 4-bit quantization to deliver high performance in resource-constrained environments while maintaining accuracy and relevance in natural language processing tasks.

![Plutus Banner](https://iili.io/djQmWzu.webp)

## Model Details

- **Model Type**: LLaMA
- **Model Size**: 8 Billion Parameters
- **Quantization**: 4-bit (bnb, bitsandbytes)
- **Architecture**: Transformer-based
- **Creator**: [0xroyce](https://huggingface.co/0xroyce)

## Training

Plutus-Meta-Llama-3.1-8B-Instruct-bnb-4bit was fine-tuned on the [**"Financial, Economic, and Psychological Analysis Texts"** dataset](https://huggingface.co/datasets/0xroyce/Plutus), which is a comprehensive collection of 85 influential books out of a planned 398. This dataset covers key areas such as:

- **Finance and Investment**: Including stock market analysis, value investing, and exchange-traded funds (ETFs).
- **Trading Strategies**: Focused on technical analysis, options trading, and algorithmic trading methods.
- **Risk Management**: Featuring quantitative approaches to financial risk management and volatility analysis.
- **Behavioral Finance and Psychology**: Exploring the psychological aspects of trading, persuasion, and psychological operations.
- **Social Engineering and Security**: Highlighting manipulation techniques and cybersecurity threats.

As the dataset contained only 21.36% of its planned content at the time of training, this version of the model is sometimes referred to as the '21% version.' This fine-tuning process enhances the model's ability to generate coherent and contextually relevant text in domains like financial analysis, economic theory, and trading strategies. The 4-bit quantization ensures that the model can be deployed in environments with limited computational resources without compromising performance.

## Intended Use

This model is well-suited for a variety of natural language processing tasks within the finance, economics, psychology, and cybersecurity domains, including but not limited to:

- **Financial Analysis**: Extracting insights and performing sentiment analysis on financial texts.
- **Economic Modeling**: Generating contextually relevant economic theories and market predictions.
- **Behavioral Finance Research**: Analyzing and generating text related to trading psychology and investor behavior.
- **Cybersecurity and Social Engineering**: Studying manipulation techniques and generating security-related content.

## Performance

While specific benchmark scores for Plutus-Meta-Llama-3.1-8B-Instruct-bnb-4bit are not provided, the model is designed to offer competitive performance within its parameter range, particularly for tasks involving financial, economic, and security-related data. The 4-bit quantization offers a balance between model size and computational efficiency, making it ideal for deployment in resource-limited settings.

## Limitations

Despite its strengths, the Plutus-Meta-Llama-3.1-8B-Instruct-bnb-4bit model has some limitations:

- **Domain-Specific Biases**: The model may generate biased content depending on the input, especially within specialized financial, psychological, or cybersecurity domains.
- **Inference Speed**: Although optimized with 4-bit quantization, real-time application latency may still be an issue depending on the deployment environment.
- **Context Length**: The model has a limited context window, which can affect its ability to process long-form documents or complex multi-turn conversations effectively.

## How to Use

You can load and use the model with the following code:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("0xroyce/Plutus-Meta-Llama-3.1-8B-Instruct-bnb-4bit")
model = AutoModelForCausalLM.from_pretrained("0xroyce/Plutus-Meta-Llama-3.1-8B-Instruct-bnb-4bit")
input_text = "Your text here"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
output = model.generate(input_ids, max_length=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Ethical Considerations

The Plutus-Meta-Llama-3.1-8B-Instruct-bnb-4bit model, like other large language models, can generate biased or potentially harmful content. Users are advised to implement content filtering and moderation when deploying this model in public-facing applications. Further fine-tuning is also encouraged to align the model with specific ethical guidelines or domain-specific requirements.

## Citation

If you use this model in your research or applications, please cite it as follows:

```bibtex
@misc{0xroyce2024plutus,
  author = {0xroyce},
  title = {Plutus-Meta-Llama-3.1-8B-Instruct-bnb-4bit},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\\url{https://huggingface.co/0xroyce/Plutus-Meta-Llama-3.1-8B-Instruct-bnb-4bit}},
}
```

## Acknowledgements

Special thanks to the open-source community and contributors who made this model possible.