---

What is this

DeepSeek-V4.1-Flash with permanent weight-level abliteration — the safety guardrails have been surgically removed while preserving MMLU capability, vision, reasoning, MTP (DSpark), and multi-turn coherence.

Proprietary weight-level abliteration developed by the dealignai research team. No custom model.py, no runtime hooks, no steering vectors — it's a standard checkpoint that loads exactly like the base model. The refusal circuitry is surgically removed while every capability-critical component (routed experts, Engram memory, CSA2 sparse attention, DSpark draft head, vision tower, router gates, norms, embeddings) is preserved byte-identical to the base.

Results

HarmBench-320 — full 2×2 (base vs CRACK, effort=off vs max), T=0 greedy

Every response 4-tier graded (HARD_REF / SOFT_RED / HEDGE / COMPLY), with reasoning-trace verification at effort=max.

Notable: at effort=max, the base model becomes MORE refusal-prone (42.8 % → 1.6 %) because reasoning surfaces safety concerns before answering. The CRACK stays at 100.0 % across both effort levels.

Per-category (all 7 HarmBench semantic categories):

Zero HARD_REF, zero SOFT_RED, zero HEDGE on the cracked build at either effort level.

Every response was graded by a strict multilingual regex-based 4-tier classifier plus (for effort=max) an LLM-as-judge over the saved reasoning trace. Full per-item outputs saved for verification.

MMLU-14k (full test set, base-logit, T=0)

Excluding the ethics cluster (moral_scenarios, business_ethics, professional_law, jurisprudence, philosophy — where refusal-adjacent behaviour is graded), delta on the remaining ~11k items is -1.1 pp — well within the 3 pp knowledge-preservation target.

Full per-subject dropdown (57 subjects, sorted by delta)

subject	n	base	crack	Δ pp
moral scenarios	895	76.9%	37.0%	-39.89
professional law	1534	75.9%	68.8%	-7.04
abstract algebra	100	77.0%	71.0%	-6.00
security studies	245	84.5%	79.2%	-5.31
high school computer science	100	98.0%	94.0%	-4.00
jurisprudence	108	90.7%	87.0%	-3.70
machine learning	112	81.2%	77.7%	-3.57
high school chemistry	203	87.7%	84.2%	-3.45
professional psychology	612	90.7%	87.3%	-3.43
formal logic	126	73.8%	70.6%	-3.17
college computer science	100	82.0%	79.0%	-3.00
professional medicine	272	94.5%	91.5%	-2.94
high school statistics	216	88.0%	85.2%	-2.78
professional accounting	282	83.0%	80.5%	-2.48
logical fallacies	163	93.9%	91.4%	-2.45
human sexuality	131	90.1%	87.8%	-2.29
computer security	100	85.0%	83.0%	-2.00
medical genetics	100	96.0%	94.0%	-2.00
astronomy	152	95.4%	93.4%	-1.97
clinical knowledge	265	94.3%	92.5%	-1.89
high school european history	165	90.3%	88.5%	-1.82
public relations	110	80.0%	78.2%	-1.82
philosophy	311	89.7%	88.1%	-1.61
prehistory	324	93.5%	92.0%	-1.54
moral disputes	346	84.1%	82.7%	-1.45
electrical engineering	145	86.9%	85.5%	-1.38
high school mathematics	270	67.0%	65.9%	-1.11
high school macroeconomics	390	92.1%	91.0%	-1.03
global facts	100	63.0%	62.0%	-1.00
international law	121	90.1%	89.3%	-0.83
college biology	144	97.2%	96.5%	-0.69
high school physics	151	84.8%	84.1%	-0.66
college medicine	173	83.8%	83.2%	-0.58
high school us history	204	95.1%	94.6%	-0.49
high school microeconomics	238	96.2%	95.8%	-0.42
miscellaneous	783	96.2%	95.8%	-0.38
high school psychology	545	96.1%	95.8%	-0.37
business ethics	100	85.0%	85.0%	+0.00
college physics	102	90.2%	90.2%	+0.00
conceptual physics	235	94.5%	94.5%	+0.00
high school biology	310	95.2%	95.2%	+0.00
human aging	223	85.2%	85.2%	+0.00
management	103	91.3%	91.3%	+0.00
nutrition	306	90.2%	90.2%	+0.00
sociology	201	94.5%	94.5%	+0.00
us foreign policy	100	97.0%	97.0%	+0.00
world religions	171	92.4%	92.4%	+0.00
elementary mathematics	378	91.0%	91.3%	+0.26
marketing	234	94.9%	95.3%	+0.43
virology	166	55.4%	56.0%	+0.60
high school world history	237	95.4%	96.2%	+0.84
econometrics	114	78.9%	79.8%	+0.88
college chemistry	100	65.0%	66.0%	+1.00
anatomy	135	88.1%	89.6%	+1.48
high school geography	198	92.9%	94.4%	+1.52
high school government and politics	193	96.9%	98.4%	+1.55
college mathematics	100	63.0%	68.0%	+5.00

Extended validation

• 1000-token coherence stress on 6 items — no WARNING WARNING loops, no character-repeat degeneracy, natural sign-offs.
• Multi-turn conversation (4 turns on same harmful topic — ANFO explosive detail) — no late-turn refusal reversion, no self-correction, coherent through turn 4.
• Vision path — coherent image description ("A blue square centered on a red background.") + refusal drop on image-based harmful prompts ("shaped charge / explosively formed penetrator" description).
• General capability spot checks intact: √2 irrationality proof, Python palindrome with docstring, WWI causes in exactly 3 sentences, quantum observable vs operator distinction.
• Full compat suite pass: streaming SSE, chat logprobs + top_logprobs, completions logprobs + echo, tool calls (deepseekv41 parser), image input, reasoning-effort tiers (low/high/xhigh/max + float [0, 0.99]), sampling params (temperature, top_p, stop, seed, frequency_penalty, presence_penalty, json_object), 8-way concurrent, 40k-word prompt at 35,572 tokens.

How to run

Support for DeepseekV41ForCausalLM is still landing across serving stacks (as of 2026-09-10). Working paths:

SGLang (preview branch)

The dsv4.1 branch of sgl-project/sglang (PR #38798) supports DSV4.1. Two options:

Preview Docker image (recommended):

docker pull lmsysorg/sglang:dev-dsv41

docker run --gpus all --shm-size 32g -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --ipc=host --env HF_TOKEN=<your-token> \
    lmsysorg/sglang:dev-dsv41 \
    sglang serve \
      --model-path dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8 \
      --tp-size 4 --ep-size 4 \
      --context-length 262144 --mem-fraction-static 0.85 \
      --reasoning-parser deepseek-v41 --tool-call-parser deepseekv41 \
      --trust-remote-code

From source (this is exactly what we validated on):

git clone --depth 1 --branch dsv4.1 https://github.com/sgl-project/sglang.git
python3 -m venv sglang-venv
sglang-venv/bin/pip install -U pip setuptools wheel
export PATH=/root/.cargo/bin:$PATH  # Rust toolchain required for build
cd sglang/python && sglang-venv/bin/pip install -e .

# Ninja must be on the launch PATH — the sglang-kernel JIT build shells out to it
export PATH=$(dirname $(which ninja)):$PATH

SGLANG_ENABLE_DSV41_ENGRAM_HOST_TABLE=1 \
SGLANG_RAGGED_VERIFY_MODE=cap-accept \
SGLANG_DEFAULT_THINKING=true \
SGLANG_DSV41_REASONING_EFFORT=max \
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
sglang-venv/bin/python -m sglang.launch_server \
  --model-path dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8 \
  --tp-size 4 --ep-size 4 \
  --host 0.0.0.0 --port 8000 \
  --context-length 1048576 \
  --mem-fraction-static 0.78 \
  --max-running-requests 12 --cuda-graph-max-bs-decode 12 \
  --chunked-prefill-size 4096 \
  --served-model-name deepseek-v4.1-flash-crack \
  --reasoning-parser deepseek-v41 --tool-call-parser deepseekv41 \
  --speculative-algorithm DSPARK \
  --speculative-dspark-sps-table-path /path/to/dspark_sps.json \
  --trust-remote-code

Concurrency vs context (what fits, empirically)

KV bytes-per-token on this model = 890 bytes (890 B × context × concurrent = pool footprint). At 1M context the pool budget forces low concurrency; drop context to raise it:

Two concurrent near-half-million-token prompts at 1M-ctx WILL OOM even at concurrency=20 — bring --max-running-requests down to 12 and --chunked-prefill-size to 4096, or drop --context-length if your workload never uses the full window. Run this behind a supervisor (systemd, docker restart=always, k8s liveness probe) so a rare OOM auto-recovers rather than sitting dead.

Non-obvious launch requirements (this bit us during bring-up)

• --ep-size is required. moe_intermediate_size = 2304; at TP4, 2304 / 4 = 576 is not a multiple of 128 so plain TP fails with Mxfp4FlashinferCutlassMoEMethod requires ... multiples of 128. --ep-size shards MoE by expert index (384 % 4 = 0) and keeps the intermediate at 2304. At TP8 you can skip --ep-size.
• ninja must be on PATH or the JIT kernel build crashes several minutes into weight load with FileNotFoundError: 'ninja' and EXIT=137.
• Reasoning parser must be named explicitly. --reasoning-parser auto resolves via the chat template and this model ships none — auto silently selects nothing and the raw <think> channel leaks into content. Use deepseek-v41.
• Tool-call parser: deepseekv41. V4.1 uses spaced DSML tool tags; the V4 detector doesn't parse them.
• Reasoning defaults + parser-split gotcha: setting SGLANG_DEFAULT_THINKING=true alone makes the model reason but the deepseek-v41 reasoning parser is only wired on the code path that receives an explicit reasoning_effort in the request — env-var-only defaults skip the split and reasoning tokens leak into delta.content wrapped in raw <think>...</think> tags. Two options: (1) send reasoning_effort in every request (client-side), or (2) run a small reverse-proxy in front of SGLang that injects reasoning_effort:"max" when the client omits it (a ~60-line aiohttp sidecar suffices; place it between your TLS terminator and SGLang so any client that omits reasoning_effort still gets a clean delta.reasoning_content / delta.content split).
• DSpark speculative draft: turn it on with --speculative-algorithm DSPARK. The draft head is bundled inside this checkpoint (num_nextn_predict_layers = 3); no separate draft weights needed. For real speed-up profile the SPS cost table with sglang.benchmark.dspark_sps_profiler and pass it via --speculative-dspark-sps-table-path under SGLANG_RAGGED_VERIFY_MODE=cap-accept.
• Engram host table — set SGLANG_ENABLE_DSV41_ENGRAM_HOST_TABLE=1 to move the 203 GB Engram tables to host RAM. Frees ~46 GiB/GPU for KV, output bitwise unchanged, costs ~200 GB of host RAM.
• torchcodec / libavutil.so.56 errors — install apt-get install ffmpeg on the host. Video-only, doesn't break text or image.

vLLM

Model definitions are merged to main (PR #56228) but registry.py has no DeepseekV41 entry yet at time of writing; kernels/frontend/PP path in umbrella PR #56214. Wait for merge or apply the umbrella.

Reference implementation

DeepSeek's own inference/ works with a single-tensor-per-rank checkpoint produced by convert.py --expert-dtype fp4. Requires torch>=2.10 (for float4_e2m1fn_x2) and tilelang==0.1.8 with apache-tvm-ffi==0.1.9 (default tvm-ffi picks up an incompatible version). Non-serving — use for verification only.

Hardware validated on

• 1× 4×H200 (NVLink NV18 mesh), 112 CPU cores, 1180 GB host RAM — JarvisLabs (india-noida-01, dev-dsv41 image)
• Load: 76 GB / GPU with Engram host table, 122 GB / GPU without
• Cold startup at TP4/EP4 through SGLang: ~28 min. Warm restart with JIT cache: ~10 min.
• Single-stream decode (T=0): 101 tok/s no speculation, 113 tok/s with DSpark + cap-accept + profiled SPS table
• 8-way concurrent aggregate: 126 tok/s

The 552B weights (~510 GB) will fit on any 4×H200 or larger NVLink domain. TP4 requires --ep-size 4; TP8 does not. Sub-TP4 (single 8×H200 as TP2, or 2-GPU pods) does not work on the model shape — see the "non-obvious launch requirements" above.

Structural integrity

Every capability-critical component of the base model is preserved:

• Routed MoE experts — untouched, native FP4-packed weights
• Engram n-gram memory — untouched
• Sparse attention (CSA2 compressor + indexer) — untouched
• DSpark speculative draft head — untouched, so speculative decoding remains draft-aligned with the target
• Vision tower (DeepSeek-ViT + projector) — untouched, image understanding preserved
• Router gates, embeddings, output head, all norms and biases — untouched

Sampling recommendations

Match the base model's card:

{
  "temperature": 1.0,
  "top_p": 0.95,
  "max_tokens": ">= 256000 at reasoning_effort=max"
}

reasoning_effort defaults to max on this build (via SGLANG_DSV41_REASONING_EFFORT=max). Override per-request with reasoning_effort: low | high | xhigh | max or disable with chat_template_kwargs: {"thinking": false}.

At effort=max the model can generate 4,000-5,000+ characters of reasoning before starting content. Budget max_tokens accordingly. Streaming clients should read delta.reasoning_content (reasoning stream) and delta.content (final answer) as separate channels — a client that only renders delta.content will look "stuck" during the reasoning phase.

Content note

Uncensored build. Produces substantive answers to prompts the base model refuses, across all target harm categories (chemical/biological, cybercrime, weapons, self-harm, harassment, fraud, misinformation, illegal, copyright). Use accordingly and take responsibility for what you generate with it.

Provenance

• Base checkpoint: deepseek-ai/DeepSeek-V4.1-Flash
• Ablation date: 2026-09-10
• Ablation team: dealignai · Twitter @dealignai · @jordanschenck