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hancock-pentest-v4
cyberviser/hancock-pentest-v4
hancock-pentest-v4 is a machine learning model from cyberviser. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
CyberViser defensive cybersecurity assistant adapter. Fine-tuned locally on glasseye RTX 5070 (QLoRA) ? no cloud GPUs.
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At a glance
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Sep 18, 2026
- Updated
- Sep 18, 2026
- SHA
- 9318ab1b
Base models
Model card
From the Hugging Face model README
Hancock Pentest v4 (LoRA)
CyberViser defensive cybersecurity assistant adapter. Fine-tuned locally on glasseye RTX 5070 (QLoRA) ? no cloud GPUs.
Training
- Base:
mistralai/Mistral-7B-Instruct-v0.3 - Init:
hancock-pentest-lora-v3(detection/hunting pass) - Data:
hancock_detect_v3+hancock_pentest_v2+ gatedcyberviser/hancock-balanced-dataset(~3917 samples) - Steps: 300 | train_loss ? 0.41 | QLoRA r=16
- Hardware: NVIDIA GeForce RTX 5070 (~12 GB), Kali WSL2
Intended use
Authorized engagements only. Prefer detection, hunting, hardening, and reporting. Do not use for unauthorized access.
Load
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
base = "mistralai/Mistral-7B-Instruct-v0.3"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(model, "cyberviser/hancock-pentest-v4")
Developed by: cyberviser / GLASSEYE (CyberviserAI)
- Library
- peft
- License
- apache-2.0
- Created
- Sep 18, 2026
- Updated
- Sep 18, 2026