vtriple
/

Llama-3.1-8B-yara

@@ -1,136 +1,32 @@
----
-base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
-library_name: peft
----
-# 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
-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:** [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]
-- **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
-### Direct Use
-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 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
-- 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
-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 following code to get started with the model:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
-from peft import PeftModel, PeftConfig
-# 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)
-# Load the tokenizer
-tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
 # 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))
-```
-## Training Details
-### Training Data
-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:** bf16 mixed precision
-- **Optimizer:** AdamW
-- **Learning rate:** 5e-5
-- **Batch size:** 4
-- **Gradient accumulation steps:** 4
-- **Epochs:** 5
-- **Max steps:** 4000
-## Evaluation
-I used a custom yara evulation
-## Environmental Impact
-- **Hardware Type:** NVIDIA A100
-- **Hours used:** 12 Hours
-- **Cloud Provider:** vast.io
-## Technical Specifications [optional]
-### 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.
-### Compute Infrastructure
-#### Hardware
-"Single NVIDIA A100 GPU"
-#### Software
-- Python 3.8+
-- PyTorch 2.0+
-- Transformers 4.28+
-- PEFT 0.12.0
-## Model Card Authors [optional]
-Wyatt Roersma
-## Model Card Contact
-Email me at [email protected] 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.
-</answer>

+# Yara-Focused Llama 3.1 8B
+This model is a fine-tuned version of Meta's Llama 3.1 8B, specifically tailored for yara tasks.
 ## Model Details
+- **Base model:** meta-llama/Meta-Llama-3.1-8B-instruct
+- **Fine-tuning:** This model has been fine-tuned on a custom dataset of cybersecurity-related questions and answers.
+- **Usage:** This model is particularly yara-focused and can generate responses to yara-related prompts.
+## How to Use
+To use this model:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
+model_name = "vtriple/Llama-3.1-8B-yara"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 # Example usage
+input_text = "What is an example of a common cybersecurity threat?"
+input_ids = tokenizer(input_text, return_tensors="pt").input_ids
+output = model.generate(input_ids, max_length=100)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+Limitations
+Please note that while this model has been fine-tuned for cybersecurity tasks, it may still produce incorrect or biased information. Always verify important information with authoritative sources.
+License
+This model inherits its license from the original Llama 3.1 8B model. Please refer to Meta's licensing terms for the Llama model family.