Dedicated Endpoints
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Container
Run this model inference with full control and performance in your environment.
Get help setting up a custom Dedicated Endpoints.
Talk with our engineer to get a quote for reserved GPU instances with discounts.
README
License: mitLoad it
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizermodel = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")model = PeftModel.from_pretrained(model, "Xx-Vexento-xX/security-testing-agent-lora")tokenizer = AutoTokenizer.from_pretrained("Xx-Vexento-xX/security-testing-agent-lora")
Training
- 15 curriculum rounds across Python, JavaScript, Java, Go, PHP, Ruby, TypeScript, Rust, Kotlin, C#
- Vulnerability types: SQL Injection, XSS, CSRF, SSRF, Command Injection, IDOR, XXE, JWT weaknesses, Path Traversal, Prototype Pollution, and more
- RAG-aware: recognizes [REFERENCE CONTEXT] blocks for external knowledge injection
Intended use
Authorized defensive security testing of your own code only. Not for unauthorized access or offensive purposes.
Model provider
Xx-Vexento-xX
Model tree
Base
meta-llama/Llama-3.1-8B-Instruct
Adapter
this model
Modalities
Input
Text
Output
Text
Pricing
Dedicated Endpoints
View detailsSupported Functionality
Model APIs
Dedicated Endpoints
Container
More information