Update README.md

README.md

@@ -21,7 +21,7 @@ base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
 
 # 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-Meta-Llama-3.1-8B-Instruct-bnb-4bit** is a fine-tuned version of the LLaMA-3.1-8B model, specifically optimized for tasks in finance, economics, trading, psychology, and social engineering. This model leverages the LLaMA architecture, enhanced with 4-bit quantization to deliver high performance in resource-constrained environments, while maintaining accuracy and relevance for natural language processing tasks in these domains.
 
 ![Plutus Banner](https://iili.io/djQmWzu.webp)
 
@@ -35,39 +35,39 @@ Plutus-Meta-Llama-3.1-8B-Instruct-bnb-4bit is a fine-tuned version of the LLaMA-
 
 ## 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 219 influential books out of a planned 398. This dataset covers key areas such as:
+The model was fine-tuned on the comprehensive [**"Financial, Economic, and Psychological Analysis Texts"** dataset](https://huggingface.co/datasets/0xroyce/Plutus), which consists of 394 books covering key areas like:
 
-- **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.
+- **Finance and Investment**: Stock market analysis, value investing, bonds, and exchange-traded funds (ETFs).
+- **Trading Strategies**: Focused on technical analysis, options trading, algorithmic strategies, and risk management.
+- **Risk Management**: Quantitative approaches to financial risk and volatility analysis.
+- **Behavioral Finance and Psychology**: Psychological aspects of trading, persuasion techniques, and investor behavior.
+- **Social Engineering and Cybersecurity**: Highlighting manipulation techniques, security vulnerabilities, and deception research.
+- **Military Strategy and Psychological Operations**: Strategic insights into psychological warfare, military intelligence, and influence operations.
 
+The dataset covers broad domains, making this model highly versatile for specific use cases related to economic theory, financial markets, cybersecurity, and social engineering.
 
 ## 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:
+Plutus-Meta-Llama-3.1-8B-Instruct-bnb-4bit is suitable for a wide variety of natural language processing tasks, particularly in finance, economics, psychology, and cybersecurity. Common use cases include:
 
-- **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.
+- **Financial Analysis**: Extract insights and perform sentiment analysis on financial documents.
+- **Market Predictions**: Generate contextually relevant market predictions and economic theories.
+- **Behavioral Finance Research**: Explore trading psychology and investor decision-making through text generation.
+- **Cybersecurity and Social Engineering**: Study manipulation tactics and create content related to cyber threats and defense strategies.
 
 ## 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.
+While benchmark scores specific to Plutus-Meta-Llama-3.1-8B-Instruct-bnb-4bit are not available, it has been optimized to balance computational efficiency and accuracy. The 4-bit quantization allows the model to operate in resource-constrained environments, making it ideal for use cases where hardware limitations are a factor.
 
 ## 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.
+- **Domain-Specific Bias**: As the model is trained on specialized data, it may generate biased content, particularly in the areas of finance, psychology, and social engineering.
+- **Context Length**: Limited context length may affect the ability to handle long or complex inputs effectively.
+- **Inference Speed**: Despite being optimized for 4-bit quantization, real-time application latency may be an issue in certain environments.
 
 ## How to Use
 
-You can load and use the model with the following code:
+You can load and use the model with the following Python code:
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer