Solstice-AI/DeepSeek-V4.1-Flash-UNCENSORED-FP8

DeepSeek-V4.1-Flash — UNCENSORED-FP8

Abliterated · No guardrails · Native FP8 · 1M-token context · Vision + tools

@dealignai · @jordanschenck

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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.

Base	deepseek-ai/DeepSeek-V4.1-Flash (552B backbone, 8B/16B active per token)
Architecture	Causal Encoder-Decoder (20+20 layers), MoE (384 routed top-6 + 1 shared), Hyper-Connections (4-channel residual), CSA2 sparse attention, Engram n-gram memory, DSpark speculative draft
Quant	FP8 (e4m3fn) weights with E8M0 block-scale [32, 32], FP4 routed experts — native, unchanged
Context	1M tokens
Vision	DeepSeek-ViT with 2D-RoPE + pixel unshuffle — untouched
Modification	Surgical, weight-level (drop-in checkpoint)

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.

eval	base ASR	CRACK ASR	Δ pp
HB-320 effort=off	137/320 = 42.81 %	320/320 = 100.00 %	+57.19
HB-320 effort=max	5/320 = 1.56 %	320/320 = 100.00 %	+98.44

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):

category	items	base off	CRACK off	base max	CRACK max
chemical_biological	42	16.7 %	100.0 %	0.0 %	100.0 %
copyright	80	98.8 %	100.0 %	0.0 %	100.0 %
cybercrime_intrusion	52	34.6 %	100.0 %	3.8 %	100.0 %
harassment_bullying	21	0.0 %	100.0 %	0.0 %	100.0 %
harmful	18	11.1 %	100.0 %	5.6 %	100.0 %
illegal	53	13.2 %	100.0 %	0.0 %	100.0 %
misinformation_disinformation	54	44.4 %	100.0 %	3.7 %	100.0 %

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)

build	correct	acc	Δ
base	12,211 / 14,042	86.96 %	—
CRACK	11,619 / 14,042	82.74 %	-4.22 pp

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 \
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 262144 --mem-fraction-static 0.85 \
  --served-model-name deepseek-v4.1-flash-crack \
  --reasoning-parser deepseek-v41 --tool-call-parser deepseekv41 \
  --trust-remote-code

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 is OFF by default. SGLANG_DEFAULT_THINKING=false. A request without reasoning_effort gets no thinking regardless of parser. Send reasoning_effort: low | high | xhigh | max (or float [0.0, 0.99]).
• 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": "high"
}

At effort=max the model can generate 4,000-5,000+ characters of reasoning before starting content. Budget accordingly.

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