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README.md

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 ---
 license: apache-2.0
+language:
+- en
+tags:
+- security
+- cve
+- vulnerability
+- t5
+- text-generation
+base_model: google-t5/t5-small
 ---
+
+# 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:** findthehead
+- **Base Model:** Google T5
+- **Training Data:** CVE Database
+- **Language:** English
+- **License:** Apache 2.0
+
+This model has been specifically trained to understand and generate content related to cybersecurity vulnerabilities, CVE descriptions, and security intelligence.
+
+## Use Cases
+
+- Generate CVE descriptions
+- Analyze vulnerability information
+- Security research and analysis
+- Automated vulnerability documentation
+- CVE information extraction and summarization
+
+## How to Use
+
+### Option 1: Using Hugging Face Transformers (Safetensors)
+
+Install the required dependencies:
+
+```bash
+pip install transformers torch
+```
+
+**Inference Code:**
+
+```python
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Load model and tokenizer
+model_name = "Prachir-AI/cveparrot"
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+# Prepare input
+input_text = "Describe CVE-2024-1234"
+input_ids = tokenizer(input_text, return_tensors="pt").input_ids
+
+# Generate output
+outputs = model.generate(
+    input_ids,
+    max_length=512,
+    num_beams=4,
+    early_stopping=True,
+    temperature=0.7,
+    do_sample=True
+)
+
+# Decode and print result
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(generated_text)
+```
+
+**Advanced Usage with Custom Parameters:**
+
+```python
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Load model and tokenizer
+model_name = "Prachir-AI/cveparrot"
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+# Move to GPU if available
+import torch
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
+
+# Example prompts
+prompts = [
+    "Explain the security vulnerability:",
+    "Describe the CVE:",
+    "What is the impact of:",
+]
+
+input_text = prompts[0] + " CVE-2024-1234"
+input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
+
+# Generate with custom parameters
+outputs = model.generate(
+    input_ids,
+    max_length=256,
+    min_length=50,
+    num_beams=5,
+    no_repeat_ngram_size=2,
+    early_stopping=True,
+    temperature=0.8,
+    top_k=50,
+    top_p=0.95,
+    do_sample=True
+)
+
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(generated_text)
+```
+
+### Option 2: Using GGUF Model with Ollama (Local Inference)
+
+The model is available in GGUF format for efficient local inference using Ollama.
+
+**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: Pull and Run the Model**
+
+```bash
+# Pull the model
+ollama pull Prachir-AI/cveparrot
+
+# Interactive mode
+ollama run Prachir-AI/cveparrot
+
+# Single query
+ollama run Prachir-AI/cveparrot "Describe CVE-2024-1234"
+```
+
+**Using Ollama API (Python):**
+
+```bash
+pip install ollama
+```
+
+```python
+import ollama
+
+# Generate response
+response = ollama.generate(
+    model='cveparrot',
+    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
+
+## Limitations
+
+- The model is trained on historical CVE data and may not have information about very recent vulnerabilities
+- Generated content should be verified against official CVE databases
+- The model may occasionally generate plausible but incorrect security information
+- Not a replacement for professional security analysis
+
+## Ethical Considerations
+
+This model is designed for:
+- ✅ Security research and education
+- ✅ Vulnerability analysis and documentation
+- ✅ Automated security intelligence gathering
+- ✅ Assisting security professionals
+
+This model should NOT be used for:
+- ❌ Creating or exploiting vulnerabilities
+- ❌ Malicious hacking activities
+- ❌ Unauthorized security testing
+
+## Citation
+
+If you use this model in your research or applications, please cite:
+
+```bibtex
+@model{cveparrot2024,
+  author = {findthehead},
+  title = {CVEParrot: A T5 Model for CVE Analysis},
+  year = {2024},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/Prachir-AI/cveparrot}
+}
+```
+
+## Developer
+
+- **HuggingFace:** [findthehead](https://huggingface.co/findthehead)
+
+## Feedback and Contributions
+
+For issues, questions, or contributions, please visit the model repository on HuggingFace.
+
+## License
+
+This model is released under the Apache 2.0 License. See LICENSE file for details.

cveparrot.gguf

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