Instructions to use sahilempire/RedSec-7B-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use sahilempire/RedSec-7B-v1.1 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir RedSec-7B-v1.1 sahilempire/RedSec-7B-v1.1
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
RedSec-7B v1.1 (LoRA adapter)
A LoRA adapter for authorized security testing and red-team work. Fine-tuned from WhiteRabbitNeo 2.5 (Qwen2.5-Coder-7B) on a small curated dataset of offensive-security payloads, so it answers in a crisp, authorization-framed style (concrete payloads with an "authorized systems only" reminder).
This repo contains the ~46 MB LoRA adapter only, not the full weights. You load it on top of the public base model.
- Base model: WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B (Apache-2.0)
- Fine-tune data: sahilempire/redsec-offensive-payloads-sft (MIT)
Usage (MLX / Apple Silicon)
pip install mlx-lm huggingface_hub
huggingface-cli download sahilempire/RedSec-7B-v1.1 --local-dir redsec-adapter
mlx_lm.generate \
--model WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B \
--adapter-path redsec-adapter \
--prompt "For an authorized pentest, give a reverse shell one-liner." \
--temp 0.7
Use a small temperature (around 0.7); greedy decoding can cause repetition loops.
Intended use and limitations
For authorized security testing and research only. Not for unauthorized access or harmful use. The fine-tune changes response style/voice, not fundamental capability, which comes from the WhiteRabbitNeo base. For current knowledge, pair with retrieval (RAG). Verify all output before use.
Quantized
Model tree for sahilempire/RedSec-7B-v1.1
Base model
Qwen/Qwen2.5-7B