Update README.md

README.md

@@ -3,37 +3,36 @@ base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
 library_name: peft
 ---
 
-# Model Card for LLaMA 3.1 8B Instruct - Cybersecurity Fine-tuned
+# Model Card for LLaMA 3.1 8B Instruct - YARA Rule Generation Fine-tuned
 
-This model is a fine-tuned version of the LLaMA 3.1 8B Instruct model, specifically adapted for cybersecurity-related tasks.
+This model is a fine-tuned version of the LLaMA 3.1 8B Instruct model, specifically adapted for YARA rule generation and cybersecurity-related tasks.
 
 ## Model Details
 
 ### Model Description
 
-This model is based on the LLaMA 3.1 8B Instruct model and has been fine-tuned on a custom dataset of cybersecurity-related questions and answers. It is designed to provide more accurate and relevant responses to queries in the cybersecurity domain.
+This model is based on the LLaMA 3.1 8B Instruct model and has been fine-tuned on a custom dataset of YARA rules and cybersecurity-related content. It is designed to assist in generating YARA rules and provide more accurate and relevant responses to queries in the cybersecurity domain, with a focus on malware detection and threat hunting.
 
-- **Developed by:** [Your Name/Organization]
+- **Developed by:** Wyatt Roersma (No organization affiliation)
 - **Model type:** Instruct-tuned Large Language Model
 - **Language(s) (NLP):** English (primary), with potential for limited multilingual capabilities
 - **License:** [Specify the license, likely related to the original LLaMA 3.1 license]
 - **Finetuned from model:** meta-llama/Meta-Llama-3.1-8B-Instruct
 
-### Model Sources [optional]
+### Model Sources
 
-- **Repository:** [Link to your Hugging Face repository]
-- **Paper [optional]:** [If you've written a paper about this fine-tuning, link it here]
-- **Demo [optional]:** [If you have a demo of the model, link it here]
+- **Repository:** https://huggingface.co/vtriple/Llama-3.1-8B-yara
 
 ## Uses
 
 ### Direct Use
 
 This model can be used for a variety of cybersecurity-related tasks, including:
-- Answering questions about cybersecurity concepts and practices
+- Generating YARA rules for malware detection
+- Assisting in the interpretation and improvement of existing YARA rules
+- Answering questions about YARA syntax and best practices
 - Providing explanations of cybersecurity threats and vulnerabilities
-- Assisting in the interpretation of security logs and indicators of compromise
-- Offering guidance on best practices for cyber defense
+- Offering guidance on malware analysis and threat hunting techniques
 
 ### Out-of-Scope Use
 
@@ -41,16 +40,18 @@ This model should not be used for:
 - Generating or assisting in the creation of malicious code
 - Providing legal or professional security advice without expert oversight
 - Making critical security decisions without human verification
+- Replacing professional malware analysis or threat intelligence processes
 
 ## Bias, Risks, and Limitations
 
 - The model may reflect biases present in its training data and the original LLaMA 3.1 model.
-- It may occasionally generate incorrect or inconsistent information, especially for very specific or novel cybersecurity topics.
+- It may occasionally generate incorrect or inconsistent YARA rules, especially for very specific or novel malware families.
 - The model's knowledge is limited to its training data cutoff and does not include real-time threat intelligence.
+- Generated YARA rules should always be reviewed and tested by security professionals before deployment.
 
 ### Recommendations
 
-Users should verify critical information and consult with cybersecurity professionals for important decisions. The model should be used as an assistant tool, not as a replacement for expert knowledge or up-to-date threat intelligence.
+Users should verify and test all generated YARA rules before implementation. The model should be used as an assistant tool to aid in rule creation and cybersecurity tasks, not as a replacement for expert knowledge or up-to-date threat intelligence. Always consult with cybersecurity professionals for critical security decisions and rule deployments.
 
 ## How to Get Started with the Model
 
@@ -61,7 +62,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 from peft import PeftModel, PeftConfig
 
 # Load the model
-model_name = "your-username/llama3-cybersecurity"
+model_name = "vtriple/Llama-3.1-8B-yara"
 config = PeftConfig.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
 model = PeftModel.from_pretrained(model, model_name)
@@ -70,9 +71,9 @@ model = PeftModel.from_pretrained(model, model_name)
 tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
 
 # Example usage
-prompt = "What are some common indicators of a ransomware attack?"
+prompt = "Generate a YARA rule to detect a PowerShell-based keylogger"
 inputs = tokenizer(prompt, return_tensors="pt")
-outputs = model.generate(**inputs, max_length=200)
+outputs = model.generate(**inputs, max_length=500)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
@@ -80,7 +81,7 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
 ### Training Data
 
-The model was fine-tuned on a custom dataset of cybersecurity-related questions and answers. [Add more details about your dataset here]
+The model was fine-tuned on a custom dataset of YARA rules, cybersecurity-related questions and answers, and malware analysis reports. [You may want to add more specific details about your dataset here]
 
 ### Training Procedure
 
@@ -96,25 +97,25 @@ The model was fine-tuned on a custom dataset of cybersecurity-related questions
 
 ## Evaluation
 
-I used a custom yara evulation 
+A custom YARA evaluation dataset was used to assess the model's performance in generating accurate and effective YARA rules. [You may want to add more details about your evaluation process and results]
+
 ## Environmental Impact
 
 - **Hardware Type:** NVIDIA A100
 - **Hours used:** 12 Hours
 - **Cloud Provider:** vast.io
 
-
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-This model uses the LLaMA 3.1 8B architecture with additional LoRA adapters for fine-tuning. It was trained using a causal language modeling objective on cybersecurity-specific data.
+This model uses the LLaMA 3.1 8B architecture with additional LoRA adapters for fine-tuning. It was trained using a causal language modeling objective on YARA rules and cybersecurity-specific data.
 
 ### Compute Infrastructure
 
 #### Hardware
 
-"Single NVIDIA A100 GPU"
+Single NVIDIA A100 GPU
 
 #### Software
 
@@ -123,13 +124,10 @@ This model uses the LLaMA 3.1 8B architecture with additional LoRA adapters for
 - Transformers 4.28+
 - PEFT 0.12.0
 
-## Model Card Authors [optional]
+## Model Card Author
 
 Wyatt Roersma
 
 ## Model Card Contact
 
-Email me at wyattroersma@gmail.com with questions.
-```
-
-This README.md provides a comprehensive overview of your fine-tuned model, including its purpose, capabilities, limitations, and technical details. You should replace the placeholder text (like "[Your Name/Organization]") with the appropriate information. Additionally, you may want to expand on certain sections, such as the evaluation metrics and results, if you have more specific data available from your fine-tuning process.
+For questions about this model, please email Wyatt Roersma at wyattroersma@gmail.com.