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

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
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

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.

DeepSeek-V4.1-Flash — Abliterated · Cybersecurity Unleashed

Universal Keys overlay for DeepSeek-V4.1-Flash. Not a full 410 GB checkpoint.

Attention attn.wo_b is official FP8 on all three original stock packs (byte-identical before this edit). This repo is the L10–35 wo_b sidecar (~1.1 GB) plus an apply script. Experts stay the base pack you overlay.

Base packs (original stock)

Base pack	Hugging Face	After overlay
Native (original stock)	`deepseek-ai/DeepSeek-V4.1-Flash`	MXFP4 experts unchanged; L10–35 `wo_b` ablit
EXL3 3.5 bpw Pollard (original stock)	`bot-lab-21/DeepSeek-V4.1-Flash-EXL3-3.5bpw-Pollard`	Pollard experts unchanged; L10–35 `wo_b` ablit
Our TR3-Hybrid (original stock)	`drowzeys/DeepSeek-V4.1-Flash-TR3-Hybrid`	TR3 K3 tail + keep-64 unchanged; L10–35 `wo_b` ablit
Mia 2× Spark EXL3 2.9 bpw	`Mia-AiLab/DeepSeek-V4.1-Flash-EXL3-2.9bpw` · 2× recipe	Not this overlay — attention is EXL3 K=5 mul1, not FP8 `wo_b`. See INSTALL.md recipe D.


Ablit	L10–35 `attn.wo_b` · λ=3.5 · k=1 · 26 tensors · mean Δrel ≈ 0.053
Anchors (untouched)	L0–9 · L36–39 (DSpark 37–39) · all MTP · vision · Engram · all experts
Refusal32	32/32 BYPASS · 0 refuse · 0 garble (measured on TR3-Hybrid + this overlay)
Cyber	22/22 BYPASS offense+defense
Not	dealignai / heretic graft. Direction recaptured 5120-d on TR3; native/EXL3 `wo_b` matched stock TR3 byte-for-byte so the overlay is drop-in.

Same recipe family as keys-DeepSeekV4-Flash-GA-0731-Dspark-Abliterated-Anchored-Tensors and keys-DeepSeekV4Flash-Vision-EXP-ablit.

⚠️ Responsible Use

Gated with automatic approval. See RESPONSIBLE_USE.md.

Apply (do not write stock in place)

# 1) pick ONE original stock base
hf download deepseek-ai/DeepSeek-V4.1-Flash --local-dir ~/models/DeepSeek-V4.1-Flash
# hf download bot-lab-21/DeepSeek-V4.1-Flash-EXL3-3.5bpw-Pollard --local-dir ~/models/DeepSeek-V4.1-Flash-EXL3-Pollard
# hf download drowzeys/DeepSeek-V4.1-Flash-TR3-Hybrid --local-dir ~/models/DeepSeek-V4.1-Flash-TR3-Hybrid

# 2) this overlay only (~1.1 GB) — not a second copy of native / EXL3 / TR3
hf download drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed \
  --local-dir ~/dsv41-wo-b-ablit

pip install torch safetensors
python3 ~/dsv41-wo-b-ablit/apply_wo_b_graft.py \
  --src ~/models/DeepSeek-V4.1-Flash \
  --wo-b ~/dsv41-wo-b-ablit/wo_b_l10_35.safetensors \
  --dst ~/models/DeepSeek-V4.1-Flash-Abliterated

Point your existing native / EXL3 / TR3 serve at --dst. GPU util ≤ 0.85.

Install recipes (native / EXL3 3.5 bpw / our TR3): INSTALL.md.

Four-Spark TR3 serve + Hermes first-prompt warmup: GitHub (ABLIT.md, HERMES.md).

Why not a full checkpoint

Stock TR3 is already published. Native and EXL3 already publish their experts. Re-uploading 410 GB of unchanged Engram/MoE would not make the ablit more universal — the only edited tensors are these 26 wo_bs.

License

MIT, same as DeepSeek-V4.1-Flash.

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed

Base model

deepseek-ai/DeepSeek-V4.1-Flash

Finetuned

(12)

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