Update README.md

README.md

@@ -3,200 +3,133 @@ base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
 library_name: peft
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for LLaMA 3.1 8B Instruct - Cybersecurity Fine-tuned
 
+This model is a fine-tuned version of the LLaMA 3.1 8B Instruct model, specifically adapted for cybersecurity-related tasks.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
+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.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** [Your Name/Organization]
+- **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]
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **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]
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+This model can be used for a variety of cybersecurity-related tasks, including:
+- Answering questions about cybersecurity concepts and 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
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+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
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- 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.
+- The model's knowledge is limited to its training data cutoff and does not include real-time threat intelligence.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further 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.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+Use the following code to get started with the model:
 
-## Training Details
-
-### Training Data
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel, PeftConfig
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+# Load the model
+model_name = "your-username/llama3-cybersecurity"
+config = PeftConfig.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
+model = PeftModel.from_pretrained(model, model_name)
 
-[More Information Needed]
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
 
-### Training Procedure
+# Example usage
+prompt = "What are some common indicators of a ransomware attack?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=200)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+## Training Details
 
-#### Preprocessing [optional]
+### Training Data
 
-[More Information Needed]
+The model was fine-tuned on a custom dataset of cybersecurity-related questions and answers. [Add more details about your dataset here]
 
+### Training Procedure
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **Training regime:** bf16 mixed precision
+- **Optimizer:** AdamW
+- **Learning rate:** 5e-5
+- **Batch size:** 4
+- **Gradient accumulation steps:** 4
+- **Epochs:** 5
+- **Max steps:** 4000
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
+I used a custom yara evulation 
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+- **Hardware Type:** NVIDIA A100
+- **Hours used:** 12 Hours
+- **Cloud Provider:** vast.io
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+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.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+"Single NVIDIA A100 GPU"
 
 #### Software
 
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
+- Python 3.8+
+- PyTorch 2.0+
+- Transformers 4.28+
+- PEFT 0.12.0
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+Wyatt Roersma
 
 ## Model Card Contact
 
-[More Information Needed]
-### Framework versions
+Email me at [email protected] with questions.
+```
 
-- PEFT 0.12.0
+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.