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:
- 5325790b246b3478a538fc207674e1d0641498d485d7bb77c8dfa1771dcab72d
- Size of remote file:
- 1.38 kB
- SHA256:
- 1e0e71e5c9aaa220d58178094eab4988ab135b6ba1066da12d997b682a2b141f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.