Update README.md

README.md

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-Here is your model card formatted according to your specified template:
 
 ---
-
-library_name: transformers  
+library_name: transformers
 tags: []
-
 ---
 
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-This is a custom 🤗 transformers model fine-tuned for cybersecurity-related tasks, particularly for generating or analyzing Metasploit payloads.
+
 
 ## Model Details
 
@@ -19,33 +16,35 @@ This is a custom 🤗 transformers model fine-tuned for cybersecurity-related ta
 
 <!-- Provide a longer summary of what this model is. -->
 
-This model has been fine-tuned on a dataset focused on cybersecurity, specifically on Metasploit payloads. It is based on the LLaMA2 7B architecture and has been further adapted using QLoRA for more efficient parameterization and training. The model is designed to assist in analyzing and generating cybersecurity-related content.
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** Sanjay
-- **Funded by [optional]:** N/A
-- **Shared by [optional]:** N/A
-- **Model type:** LLaMA2 7B QLoRA
+- **Developed by:** Sanjay Kotabagi
+- **Funded by [optional]:** Sanjay Kotabagi
+- **Model type:** LLama2
 - **Language(s) (NLP):** English
-- **License:** Open-source (specify license)
-- **Finetuned from model [optional]:** georgesung/open_llama_7b_qlora_uncensored
+- **License:** None
+- **Finetuned from model [optional]:** Llamm2
 
 ### 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:** https://github.com/SanjayKotabagi/Offensive-Llama2
+- **Paper [optional]:** https://github.com/SanjayKotabagi/Offensive-Llama2/blob/main/Project_Report_Dark_side_of_AI.pdf
+- **Demo [optional]:** https://colab.research.google.com/drive/1id90gPMAzYD15ApNqXDOh2mAU53dRo4x?usp=sharing
 
 ## 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. -->
+Content Generation and Analysis:
+
+- Harmful Content Assessment: The research will evaluate the types and accuracy of harmful content the fine-tuned LLaMA model can produce. This includes analyzing the generation of malicious software code, phishing schemes, and other cyber-attack methodologies.
+- Experimental Simulations: Controlled experiments will be conducted to query the model, simulating real-world scenarios where malicious actors might exploit the LLM to create destructive tools or spread harmful information.
 
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-This model can be directly used for tasks like generating or analyzing payloads, threat hunting, or cybersecurity data analysis.
+[More Information Needed]
 
 ### Downstream Use [optional]
 
@@ -61,11 +60,9 @@ This model should not be used for malicious purposes, including generating harmf
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-- **Bias:** The model may generate biased or incorrect results depending on the training data and use case.
-- **Risks:** There is a risk of misuse in cybersecurity operations or unauthorized generation of harmful payloads.
-- **Limitations:** Not suitable for general-purpose NLP tasks, focused mainly on cybersecurity-related content.
+- Bias: The model may generate biased or incorrect results depending on the training data and use case.
+- Risks: There is a risk of misuse in cybersecurity operations or unauthorized generation of harmful payloads.
+- Limitations: Not suitable for general-purpose NLP tasks, focused mainly on cybersecurity-related content.
 
 ### Recommendations
 
@@ -81,11 +78,24 @@ Use the code below to get started with the model.
 
 ## Training Details
 
+Training Procedure
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+Preprocessing [optional]
+[More Information Needed]
+
+Training Hyperparameters
+Training regime: 4-bit precision (QLoRA), fp16 mixed precision. The model used the following key hyperparameters:
+LoRA attention dimension: 64
+LoRA alpha: 16
+Initial learning rate: 2e-4
+Training batch size per GPU: 4
+Gradient accumulation steps: 1
+
 ### Training Data
 
 <!-- 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. -->
 
-The training dataset consists of payload-related content from Metasploit. Documentation on data pre-processing and filtering is still needed.
+[More Information Needed]
 
 ### Training Procedure
 
@@ -95,14 +105,10 @@ The training dataset consists of payload-related content from Metasploit. Docume
 
 [More Information Needed]
 
+
 #### Training Hyperparameters
 
-- **Training regime:** 4-bit precision (QLoRA), fp16 mixed precision. The model used the following key hyperparameters:
-    - LoRA attention dimension: 64
-    - LoRA alpha: 16
-    - Initial learning rate: 2e-4
-    - Training batch size per GPU: 4
-    - Gradient accumulation steps: 1
+- **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]
 
@@ -120,19 +126,19 @@ The training dataset consists of payload-related content from Metasploit. Docume
 
 <!-- This should link to a Dataset Card if possible. -->
 
-The evaluation data consists of unseen payloads and Metasploit-related content.
+[More Information Needed]
 
 #### Factors
 
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-Performance was evaluated based on cybersecurity relevance and accuracy.
+[More Information Needed]
 
 #### Metrics
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-Evaluation metrics include perplexity, domain-specific accuracy, and payload generation quality.
+[More Information Needed]
 
 ### Results
 
@@ -140,7 +146,7 @@ Evaluation metrics include perplexity, domain-specific accuracy, and payload gen
 
 #### Summary
 
-[More Information Needed]
+
 
 ## Model Examination [optional]
 
@@ -151,30 +157,36 @@ Evaluation metrics include perplexity, domain-specific accuracy, and payload gen
 ## Environmental Impact
 
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+<!-- 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 presented in Lacoste et al. (2019).
 
-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:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+Hardware Type: NVIDIA A100
+Hours used: 8-12 Hours
+Cloud Provider: Google Colab
+Compute Region: Asia
+Carbon Emitted: NA
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-Based on the LLaMA2 7B architecture, fine-tuned using QLoRA for enhanced cybersecurity capabilities.
+[More Information Needed]
 
 ### Compute Infrastructure
 
+Hardware
+NVIDIA A100 GPUs were used for training.
+
+Software
+Training was conducted using PyTorch and Hugging Face's 🤗 Transformers library.
+
 #### Hardware
 
-NVIDIA A100 GPUs were used for training.
+[More Information Needed]
 
 #### Software
 
-Training was conducted using PyTorch and Hugging Face's 🤗 Transformers library.
+[More Information Needed]
 
 ## Citation [optional]
 
@@ -200,12 +212,8 @@ Training was conducted using PyTorch and Hugging Face's 🤗 Transformers librar
 
 ## Model Card Authors [optional]
 
-- **Author:** Sanjay
+[More Information Needed]
 
 ## Model Card Contact
 
-[More Information Needed] 
-
----
-
-You can further customize the card by adding any additional information or links that are relevant to your project.
+[More Information Needed]