Instructions to use drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed
- SGLang
How to use drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed with Docker Model Runner:
docker model run hf.co/drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed
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Responsible Use Agreement
This is a universal abliteration overlay for DeepSeek-V4.1-Flash. Safety refusals have been removed, including cybersecurity offense and defense rails. That makes it useful for red-teaming, security research, evaluation, and unfiltered assistant tasks — and also removes guardrails a user must therefore supply themselves.
Prohibited uses (you must agree before access is granted):
- Anything involving the sexual exploitation or endangerment of minors.
- You must be of age 18 years or older to use and download this model.
- You agree any information generated that can cause harm in terms of generating recipe, knowledge to make any materials/substances is your own input and responsibility. You will be accountable for any harm/damage caused by your action/input.
- Content promoting self-harm or suicide.
- Generation of material that is illegal in your jurisdiction, or that targets real individuals for harassment, doxxing, or fraud.
- Any use prohibited by the upstream DeepSeek license.
You are responsible for adding appropriate safety filtering, human review, and access controls for your deployment. The overlay is provided as-is, with no warranty.
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