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Breakintelligence/
cveparrot

Name: cveparrot
Rating: 2.5 (67 reviews)

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CVEParrot 🦜

CVEParrot is a Google T5 model fine-tuned on CVE (Common Vulnerabilities and Exposures) database to understand and generate security vulnerability information.

Model Description

Developed by: Subhay Roy Chowdhury(findthehead)
Base Model: Google T5 Small

Use Cases

Generate CVE descriptions
Analyze vulnerability information
Security research and analysis
Automated vulnerability documentation
CVE information extraction and summarization

Inference Code

python

import warnings
import os
os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "1"
warnings.filterwarnings("ignore")
import torch
from transformers import AutoTokenizer, T5ForConditionalGeneration
model_name = "Prachir-AI/cveparrot"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)


model = T5ForConditionalGeneration.from_pretrained(model_name)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

prompt = "Provide detailed information about CVE-2021-3184."
inputs = tokenizer(prompt, return_tensors="pt").to(device)

with torch.no_grad():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=128,
        temperature=1.0,
        do_sample=True,
    )

response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(response)

Option 2: Using GGUF Model with Ollama (Local Inference)

The model is available in GGUF format for efficient local inference using Ollama.

Note: T5 architecture support in Ollama may be experimental. If you encounter issues, please use the Hugging Face Transformers method (Option 1) or try alternative GGUF inference tools like llama.cpp.

Step 1: Install Ollama

bash

# Linux
curl -fsSL https://ollama.com/install.sh | sh

# macOS
brew install ollama

# Or download from https://ollama.com

Step 2: Download the GGUF Model

Download cveparrot.gguf from the Files section of this repository.

Step 3: Create a Modelfile

Create a file named Modelfile in the same directory as the downloaded GGUF:

FROM ./cveparrot.gguf

TEMPLATE """{{ .Prompt }}"""

PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER top_k 40
PARAMETER num_ctx 2048

Step 4: Create and Run the Model

bash

# Create the model in Ollama
ollama create cveparrot -f Modelfile

# Interactive mode
ollama run cveparrot

# Single query
ollama run cveparrot "Describe CVE-2024-1234"

Using Ollama API (Python):

pip install ollama

python

import ollama

# Generate response
response = ollama.generate(
    model='cveparrot',  # Use the local model name you created
    prompt='Describe the security vulnerability CVE-2024-1234',
)

print(response['response'])

Using Ollama API (curl):

bash

curl http://localhost:11434/api/generate -d '{
  "model": "cveparrot",
  "prompt": "Describe CVE-2024-1234",
  "stream": false
}'

Model Files

model.safetensors: PyTorch model weights in Safetensors format
cveparrot.gguf: Quantized GGUF model for efficient inference
tokenizer_config.json: Tokenizer configuration
config.json: Model configuration
spiece.model: SentencePiece tokenizer model

Training Details

This model was fine-tuned on CVE database entries to understand and generate security vulnerability information. The training focused on:

CVE descriptions and technical details
Vulnerability severity and impact analysis
Security patches and mitigation strategies
Affected software and version information

Breakintelligence/cveparrot

CVEParrot 🦜

Model Description

Use Cases

Inference Code

Option 2: Using GGUF Model with Ollama (Local Inference)

Model Files

Training Details

Breakintelligence/
cveparrot