Text Generation
PEFT
Safetensors
English
cybersecurity
soc-triage
threat-detection
qlora
qwen3
conversational
Instructions to use minar-svn/ThreatQwen-1.7B-Detect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minar-svn/ThreatQwen-1.7B-Detect with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-1.7B-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "minar-svn/ThreatQwen-1.7B-Detect") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3952fe2e8a66337451effa41f9d1a5837c36dfc0bfb505372d769a836b4e654
- Size of remote file:
- 11.4 MB
- SHA256:
- d7430e9138b76e93fb6f93462394d236b411111aef53cb421ba97d2691040cca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.