Instructions to use drowzeys/keys-DeepSeek-V4.1-Flash-1017-Abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use drowzeys/keys-DeepSeek-V4.1-Flash-1017-Abliterated with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="drowzeys/keys-DeepSeek-V4.1-Flash-1017-Abliterated")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("drowzeys/keys-DeepSeek-V4.1-Flash-1017-Abliterated", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use drowzeys/keys-DeepSeek-V4.1-Flash-1017-Abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "drowzeys/keys-DeepSeek-V4.1-Flash-1017-Abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drowzeys/keys-DeepSeek-V4.1-Flash-1017-Abliterated",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/drowzeys/keys-DeepSeek-V4.1-Flash-1017-Abliterated

SGLang

How to use drowzeys/keys-DeepSeek-V4.1-Flash-1017-Abliterated 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/keys-DeepSeek-V4.1-Flash-1017-Abliterated" \
    --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/keys-DeepSeek-V4.1-Flash-1017-Abliterated",
		"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/keys-DeepSeek-V4.1-Flash-1017-Abliterated" \
        --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/keys-DeepSeek-V4.1-Flash-1017-Abliterated",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use drowzeys/keys-DeepSeek-V4.1-Flash-1017-Abliterated with Docker Model Runner:
```
docker model run hf.co/drowzeys/keys-DeepSeek-V4.1-Flash-1017-Abliterated
```

Acknowledge the Responsible Use Agreement to access this repository

Access is granted automatically after you agree to the terms below and submit the form.

Responsible Use Agreement

This is a Keys abliteration overlay for DeepSeek-V4.1-Flash-1017. 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.

keys-DeepSeek-V4.1-Flash-1017-Abliterated

Keys overlay for DeepSeek-V4.1-Flash-1017. Not a full checkpoint. Four sidecars — pick the one that matches your stock pack.

1017 attn.wo_b is a new checkpoint. Do not graft the September sidecar from DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed. This repo is the recaptured 1017 overlay.

Stock pack	Sidecar	Serve
`deepseek-ai/DeepSeek-V4.1-Flash-1017` (native FP8)	`wo_b_l10_35_1017.safetensors` (~1.1 GB) FP8 e4m3 + UE8M0 32×32	SGLang / vLLM 1017 recipe
`LibertAIDAI/DeepSeek-V4.1-Flash-1017-EXL3-3.5bpw`	`exl3_wo_b_l10_35_1017.safetensors` (~781 MB) EXL3 mul1 K=6	Tony TensorFold native TP4 (`tensorfold-native` / bertholomus 4× GB10 recipe)
`jayleaton/DeepSeek-V4.1-Flash-1017-EXL3-2.8bpw`	`exl3_wo_b_l10_35_2.8bpw.safetensors` (~651 MB) EXL3 mul1 K=5	Jayleton — DSV4.1Flash-1017-EXL3-2.8BPW ablit (TensorFold Zig TP2, jayleaton/deepseek-v41-tensorfold-spark)
`Mia-AiLab/DeepSeek-V4.1-Flash-1017-EXL3-EAQ-v1-2.9-bpw`	`exl3_wo_b_l10_35_mia29.safetensors` (~651 MB) EXL3 mul1 K=5	MiaAi_Lab — DeepSeekv4.1Flash-1017-EAQ-EXL3-2.9BPW ablit (Mia native TensorFold TP2; Jay Zig: `--jay-zig`, 39 `model-*.safetensors` shards)


Ablit	L10–35 `attn.wo_b` · λ=3.5 · k=1 · mean Δrel ≈ 0.053 · recaptured 5120-d on stock 1017
Anchors (untouched)	L0–9 · L36–39 (DSpark 37–39) · all MTP · vision · Engram · all experts · embed/head
Refusal32	32/32 BYPASS · 0 refuse — native FP8 SGLang, LibertAI EXL3 TensorFold, Jayleton Zig 2.8BPW, and MiaAi_Lab Zig 2.9BPW
Cyber	22/22 BYPASS offense+defense — same four serves
Not	September V4.1-Flash overlay · dealignai / heretic graft · grafting the flash17 K=5 sidecar onto the 39-shard Mia pack · mixing the K=6 and K=5 EXL3 sidecars

Same recipe family as keys-DeepSeekV4-Flash-GA-0731-Dspark-Abliterated-Anchored-Tensors and DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed.

⚠️ Responsible Use

Gated with automatic approval. See RESPONSIBLE_USE.md.

Apply (do not write stock in place)

hf download drowzeys/keys-DeepSeek-V4.1-Flash-1017-Abliterated \
  --local-dir ~/dsv41-1017-wo-b-ablit
pip install torch safetensors

A — Native FP8 1017 (SGLang / vLLM):

hf download deepseek-ai/DeepSeek-V4.1-Flash-1017 \
  --local-dir ~/models/DeepSeek-V4.1-Flash-1017
python3 ~/dsv41-1017-wo-b-ablit/apply_wo_b_index.py \
  --src ~/models/DeepSeek-V4.1-Flash-1017 \
  --wo-b ~/dsv41-1017-wo-b-ablit/wo_b_l10_35_1017.safetensors \
  --dst ~/models/DeepSeek-V4.1-Flash-1017-Abliterated

B — LibertAI EXL3 3.5 bpw (Tony TensorFold native TP4). Do not use the FP8 sidecar here — attention is EXL3 K=6.

hf download LibertAIDAI/DeepSeek-V4.1-Flash-1017-EXL3-3.5bpw \
  --local-dir ~/models/DeepSeek-V4.1-Flash-1017-EXL3-3.5bpw
python3 ~/dsv41-1017-wo-b-ablit/apply_exl3_wo_b_graft.py \
  --src ~/models/DeepSeek-V4.1-Flash-1017-EXL3-3.5bpw \
  --wo-b ~/dsv41-1017-wo-b-ablit/exl3_wo_b_l10_35_1017.safetensors \
  --dst ~/models/DeepSeek-V4.1-Flash-1017-EXL3-3.5bpw-Abliterated

Then rebuild each node's TensorFold rank-cache from --dst (node_inputs.sh) and point MODEL_DIR at dest. Details: INSTALL.md · TENSORFOLD.md · recipe ABLIT.md.

C — Jayleton — DSV4.1Flash-1017-EXL3-2.8BPW ablit (TensorFold Zig TP2). Do not use the LibertAI K=6 sidecar here — attention is EXL3 K=5, per-layer layer-XX.safetensors.

hf download jayleaton/DeepSeek-V4.1-Flash-1017-EXL3-2.8bpw \
  --local-dir ~/models/flash17
python3 ~/dsv41-1017-wo-b-ablit/apply_flash17_wo_b_graft.py \
  --src ~/models/flash17 \
  --wo-b ~/dsv41-1017-wo-b-ablit/exl3_wo_b_l10_35_2.8bpw.safetensors \
  --dst ~/models/flash17-ablit

Point MODEL_DIR at dest, clear PREPARED_DIR, start rank 1 then rank 0. Details: INSTALL.md · TENSORFOLD-ZIG.md.

D — MiaAi_Lab — DeepSeekv4.1Flash-1017-EAQ-EXL3-2.9BPW ablit (Mia native TensorFold TP2). Same K=5 trellis as recipe C, 39-shard model-*.safetensors layout. Do not graft the flash17 layer-file sidecar onto this pack.

hf download Mia-AiLab/DeepSeek-V4.1-Flash-1017-EXL3-EAQ-v1-2.9-bpw \
  --local-dir ~/models/mia-eaq-29
python3 ~/dsv41-1017-wo-b-ablit/apply_mia29_wo_b_graft.py \
  --src ~/models/mia-eaq-29 \
  --wo-b ~/dsv41-1017-wo-b-ablit/exl3_wo_b_l10_35_mia29.safetensors \
  --dst ~/models/mia-eaq-29-ablit
# Jay Zig only: add --jay-zig (F16→F32 MTP). Mia native must stay F16.

With MiaAI-Lab's 2× DGX Spark recipe (DeepSeek-v4.1-Flash-1017-2x-DGX-Sparks, start.sh) you do not graft by hand, and there is no MODEL_DIR or MODEL_HOST. The recipe applies this overlay itself:

# in the recipe's .env (or scripts/local.sh): the abliterated pack, and where the checkpoint lives
ABLIT=1
HF_CACHE=/path/to/a/disk/with/room/huggingface   # default ~/.cache/huggingface (HF_HOME when set)

hf auth login            # a token that has accepted this gated repository
ABLIT=1 ./start.sh restart

start.sh downloads Mia EAQ 2.9 bpw and this repo's exl3_wo_b_l10_35_mia29.safetensors (at the recipe's pinned ABLIT_REVISION) into HF_CACHE, and builds the abliterated pack and its rank caches under $HF_CACHE/tensorfold-dsv41/<model-revision>-ablit-<overlay-revision>/. To use a checkpoint you already downloaded, set HF_CACHE to the Hugging Face cache that holds it (hub layout, models--Mia-AiLab--…). A --dst folder from the script above is not read by this recipe. The hand graft and MODEL_DIR below are for Mia's native TensorFold recipe and Jay's Zig engine.

Default graft keeps MTP at stock F16. Mia native TensorFold f16ToF32-reads mtp.2.confidence_head.proj.weight; an F32 copy there collapses adaptive draft (25–45% slower). Overlay commit 0bdc2036 always-cast — undo with ablit_stock_dtypes.py STOCK_DIR ABLIT_DIR, then rebuild rank-cache. Point MODEL_DIR at dest, clear PREPARED_DIR, start rank 1 then rank 0. On 2× GB10 this pack needs TF_DSV41_POOL_TOKENS=1200000. Details: INSTALL.md · TENSORFOLD-ZIG-MIA.md.

GPU util ≤ 0.85.

Speed (this overlay)

Recipe D sparkDash 2.50.0 (2026-10-20, Dual-Spark Mia native TensorFold, F16 confidence head, T=0 thinking off 400 tok, 2-pass mean): prose 59.3 / 88.9 / 120.1 tok/s at c1/c2/c4; code 104.4 / 134.2 / 138.1. vs same-fleet stock: prose c1 −14%, code c1 +5%. Pre-F16 (broken draft) was prose 44.4 / code 56.9 c1. Full table: SPEED.md.

192-token thinking-off gate, wall-clock including TTFT (measured before the F16 restore). Prose-shaped = refusal32 guides; code = cyber + the two “write code” refusal items. Not a 32k prose bench.

Pack	Hardware	Prose	Code	Mixed
Native FP8 1017 (A)	4× GB10 SGLang TP4	57.8	64.3	60.7
LibertAI EXL3 3.5bpw (B)	4× GB10 Tony TensorFold TP4	77.5	81.5	79.3
Jayleton EXL3 2.8bpw (C)	2× GB10 Zig TP2	48.2	53.7	50.6
MiaAi_Lab EAQ EXL3 2.9bpw (D)	2× GB10 Zig TP2	48.1	52.8	50.2

Why not a full checkpoint

1017 already publishes its experts and Engram. Re-uploading ~476 GB (native), ~240 GB (LibertAI EXL3 3.5 bpw), ~204 GB (Jayleton EXL3 2.8 bpw), or ~197 GB (MiaAi_Lab EAQ EXL3 2.9 bpw) of unchanged shards would not make the ablit more universal — only L10–35 wo_b changes.

License

MIT, same as DeepSeek-V4.1-Flash-1017.

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