Instructions to use GLASSEYE/vector-bbp-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use GLASSEYE/vector-bbp-v1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3") model = PeftModel.from_pretrained(base_model, "GLASSEYE/vector-bbp-v1") - Notebooks
- Google Colab
- Kaggle
File size: 758 Bytes
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license: apache-2.0
tags:
- vector
- bug-bounty
- hackerone
- bbp
- vdp
- pentest
- peft
- lora
base_model: mistralai/Mistral-7B-Instruct-v0.3
---
# VECTOR BBP/VDP Analyst (LoRA v1)
Authorized **bug bounty (BBP)**, **vulnerability disclosure (VDP)**, and contracted **pentest** copilot.
- Fine-tuned **only** on local RTX 5070 (glasseye) — no cloud GPUs
- Continued from GlassEye BBP v1 with VECTOR Analyst system prompt + auth hardening (no invented program IDs)
- Owner: Johnny Watters / cyberviser / GLASSEYE (0AI)
## Intended use
Scope parsing, in-scope methodology, CVSS/CWE report drafting, coordinated disclosure coaching.
## Not for
Unauthorized testing, ransomware, phishing kits, credential theft, production data dumps.
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