Update README.md

README.md

@@ -14,9 +14,11 @@ model_creator: 0xroyce
 model_type: LLaMA
 ---
 
-# 0xroyce/Plutus-Meta-Llama-3.1-8B-Instruct-bnb-4bit
+# 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.
+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
 
@@ -29,7 +31,7 @@ model_type: LLaMA
 
 ## 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:
+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.
@@ -50,11 +52,11 @@ This model is well-suited for a variety of natural language processing tasks wit
 
 ## 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.
+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 0xroyce/Plutus-Meta-Llama-3.1-8B-Instruct-bnb-4bit model has some 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.
@@ -79,7 +81,7 @@ 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.
+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