Create README.md

README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- llama
+- transformer
+- 8b
+- 4bit
+- instruction-tuning
+- conversational
+pipeline_tag: text-generation
+inference: false
+model_creator: 0xroyce
+model_type: LLaMA
+---
+
+# 0xroyce/Plutus-Meta-Llama-3.1-8B-Instruct-bnb-4bit
+
+0xroyce/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.
+
+## Model Details
+
+- **Model Type**: LLaMA
+- **Model Size**: 8 Billion Parameters
+- **Quantization**: 4-bit (bnb, bitsandbytes)
+- **Architecture**: Transformer-based
+- **Creator**: [0xroyce](https://huggingface.co/0xroyce)
+- **License**: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
+## Training
+
+0xroyce/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 0xroyce/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 0xroyce/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 0xroyce/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.