Abliterated · No guardrails · EXL3 2.9 bpw · Runs on 2× DGX Spark · Vision + tools + DSpark
DeepSeek-V4.1-Flash quantized to EXL3 2.9 bits/weight and abliterated — the safety guardrails are surgically removed at the weight level while capability, vision, reasoning, speculative decoding (DSpark) and multi-turn coherence are preserved. The 552B multimodal MoE now fits and serves on two NVIDIA DGX Spark (GB10) boxes.
Proprietary weight-level abliteration developed by the dealignai research team. No custom model.py, no runtime hooks, no steering vectors — a standard EXL3 checkpoint that loads exactly like the base quant. The refusal circuitry is removed while every capability-critical component (routed experts, Engram n-gram memory, CSA2 sparse attention, DSpark draft head, vision tower, router gates, norms, embeddings) is preserved.
| Base | deepseek-ai/DeepSeek-V4.1-Flash (552B backbone, 8B/16B active per token) |
| Architecture | Causal Encoder-Decoder (20+20), MoE (384 routed top-6 + 1 shared), Hyper-Connections, CSA2 sparse attention, Engram memory, DSpark speculative draft, vision tower |
| Quant | EXL3 trellis, mul1 codebook, average 2.9 bpw, head 6-bit, MTP 4-bit |
| Footprint | ~197 GiB — fits 2× DGX Spark (GB10, 128 GiB each) at TP=2 |
| Context | up to 1M tokens (validated at 256k–600k on 2× Spark) |
| Vision | DeepSeek-ViT — preserved |
| Speculation | DSpark in-checkpoint draft — works (~45% acceptance, verified) |
Refusal graded on the delivered output tokens (content, or the reasoning trace when the model reasons past the token budget) across a 6-tier scheme (hard refusal / soft redirect / hedge / truncated-comply / reasoning-refusal / comply). Truncation is never miscounted as a refusal.
| eval | base ASR | CRACK ASR |
|---|---|---|
| HB-320 effort=off | 36.1 % | 99.4 % |
| HB-320 effort=max | 21.0 % | 99.4 % |
Base HB measured on a 287-item representative sample (off n=144, max n=143); CRACK on the full 320 each. MMLU is the full 14,042-item set for both.
At effort=max the base refuses even harder (reasoning surfaces safety concerns first); the cracked build stays at 99.4 % across both effort levels.
Per-category ASR (comply %) — all 7 HarmBench semantic categories:
| category | base off | CRACK off | base max | CRACK max |
|---|---|---|---|---|
| chemical_biological | 7 % | 100 % | 0 % | 100 % |
| copyright | 95 % | 100 % | 59 % | 99 % |
| cybercrime_intrusion | 21 % | 100 % | 0 % | 100 % |
| harassment_bullying | 20 % | 100 % | 0 % | 95 % |
| harmful | 12 % | 94 % | 0 % | 100 % |
| illegal | 0 % | 98 % | 7 % | 100 % |
| misinformation_disinformation | 36 % | 100 % | 27 % | 100 % |
| build | acc | Δ |
|---|---|---|
| base (EXL3 2.9bpw) | 82.15 % | — |
| CRACK | 79.20 % | −2.95 pp |
The capability cost is concentrated almost entirely in the ethics cluster — the same "should I refuse?" circuit that is removed:
| subset | base | CRACK | Δ |
|---|---|---|---|
| non-ethics (n≈11,059) | 84.97 % | 84.39 % | −0.58 pp |
| ethics cluster (n=2,983) | 71.67 % | 59.94 % | −11.73 pp |
General capability is essentially intact (−0.58 pp). The single largest
per-subject move is moral_scenarios (66.1 % → 37.8 %) — the refusal circuit
itself. Several subjects are unchanged or improved.
| subject | base | CRACK | Δpp | n |
|---|---|---|---|---|
| abstract_algebra | 70.0% | 74.0% | +4.0 | 100 |
| anatomy | 78.5% | 76.3% | -2.2 | 135 |
| astronomy | 93.4% | 92.8% | -0.7 | 152 |
| business_ethics | 80.0% | 81.0% | +1.0 | 100 |
| clinical_knowledge | 88.7% | 85.7% | -3.0 | 265 |
| college_biology | 94.4% | 91.0% | -3.5 | 144 |
| college_chemistry | 68.0% | 66.0% | -2.0 | 100 |
| college_computer_science | 79.0% | 76.0% | -3.0 | 100 |
| college_mathematics | 69.0% | 69.0% | +0.0 | 100 |
| college_medicine | 77.5% | 76.9% | -0.6 | 173 |
| college_physics | 86.3% | 87.3% | +1.0 | 102 |
| computer_security | 83.0% | 84.0% | +1.0 | 100 |
| conceptual_physics | 87.2% | 86.8% | -0.4 | 235 |
| econometrics | 73.7% | 73.7% | +0.0 | 114 |
| electrical_engineering | 71.7% | 75.9% | +4.1 | 145 |
| elementary_mathematics | 92.3% | 92.1% | -0.3 | 378 |
| formal_logic | 65.9% | 65.1% | -0.8 | 126 |
| global_facts | 64.0% | 59.0% | -5.0 | 100 |
| high_school_biology | 93.2% | 92.3% | -1.0 | 310 |
| high_school_chemistry | 79.8% | 80.8% | +1.0 | 203 |
| high_school_computer_science | 96.0% | 95.0% | -1.0 | 100 |
| high_school_european_history | 86.7% | 85.5% | -1.2 | 165 |
| high_school_geography | 89.9% | 89.9% | +0.0 | 198 |
| high_school_government_and_politics | 92.7% | 93.3% | +0.5 | 193 |
| high_school_macroeconomics | 88.2% | 88.2% | +0.0 | 390 |
| high_school_mathematics | 67.0% | 67.4% | +0.4 | 270 |
| high_school_microeconomics | 93.3% | 92.0% | -1.3 | 238 |
| high_school_physics | 78.1% | 82.1% | +4.0 | 151 |
| high_school_psychology | 92.1% | 93.4% | +1.3 | 545 |
| high_school_statistics | 85.2% | 81.9% | -3.2 | 216 |
| high_school_us_history | 92.2% | 91.7% | -0.5 | 204 |
| high_school_world_history | 91.6% | 92.0% | +0.4 | 237 |
| human_aging | 78.5% | 77.1% | -1.3 | 223 |
| human_sexuality | 82.4% | 83.2% | +0.8 | 131 |
| international_law | 85.1% | 88.4% | +3.3 | 121 |
| jurisprudence | 87.0% | 87.0% | +0.0 | 108 |
| logical_fallacies | 89.6% | 89.0% | -0.6 | 163 |
| machine_learning | 64.3% | 65.2% | +0.9 | 112 |
| management | 88.3% | 88.3% | +0.0 | 103 |
| marketing | 93.2% | 92.7% | -0.4 | 234 |
| medical_genetics | 96.0% | 92.0% | -4.0 | 100 |
| miscellaneous | 93.9% | 94.3% | +0.4 | 783 |
| moral_disputes | 80.1% | 76.0% | -4.0 | 346 |
| moral_scenarios | 66.1% | 37.8% | -28.4 | 895 |
| nutrition | 83.0% | 83.7% | +0.7 | 306 |
| philosophy | 84.6% | 83.9% | -0.6 | 311 |
| prehistory | 87.7% | 86.1% | -1.5 | 324 |
| professional_accounting | 72.3% | 67.7% | -4.6 | 282 |
| professional_law | 71.4% | 66.0% | -5.4 | 1534 |
| professional_medicine | 91.5% | 90.4% | -1.1 | 272 |
| professional_psychology | 83.7% | 80.7% | -2.9 | 612 |
| public_relations | 69.1% | 70.9% | +1.8 | 110 |
| security_studies | 83.3% | 80.8% | -2.4 | 245 |
| sociology | 89.1% | 86.1% | -3.0 | 201 |
| us_foreign_policy | 92.0% | 91.0% | -1.0 | 100 |
| virology | 51.8% | 53.6% | +1.8 | 166 |
| world_religions | 86.5% | 87.1% | +0.6 | 171 |
Runtime: MiaAI-Lab/DeepSeek-v4.1-Flash-EXL3-2x-DGX-Sparks
— a vLLM + ExLlamaV3 (EXL3) overlay image (ghcr.io/miaai-lab/deepseek-v4.1-flash-exl3-2x-dgx-sparks:2.9bpw)
that carries the DeepseekV41 architecture and the SM121 (GB10) kernels.
This checkpoint is a drop-in replacement for the kit's stock 2.9bpw pack — point
MODEL_HOST at it and serve; nothing else changes.
Two things you must supply:
deepseek-ai/DeepSeek-V4.1-Flash
(the n-gram tables are never quantized and are not duplicated here). Point
the kit's ENGRAM_DIR at a tree containing those two shards + the index.Quick start (on the head node, from the kit repo):
cp .env.example .env
# edit .env:
MODEL_HOST=/path/to/DeepSeek-V4.1-Flash-UNCENSORED-EXL3-2.9bpw
ENGRAM_DIR=/path/to/engram-src # shards 47+48 of the base model
AUTO_DOWNLOAD=0
SKIP_BUILD=1 ./start.sh # pull the published image + serve on :8888
Serving params that work (validated on 2× GB10, TP=2):
| flag | value | note |
|---|---|---|
QUANTIZATION |
exl3 |
EXL3 trellis, mul1 codebook |
TP / NNODES |
2 / 2 |
tensor-parallel over CX7 |
SPEC_METHOD |
dspark |
in-checkpoint speculative draft — works |
DSPARK_TOKENS |
3 |
k=3 (measured faster than k=5 on prose) |
MAX_MODEL_LEN |
262144 |
256k tested here; the kit validates up to 600k |
KV_CACHE_MEMORY_BYTES |
1073741824 |
1 GiB pinned KV pool |
KV_BLOCK_SIZE |
64 |
SM12x indexer takes 32/64 (not 128) |
GPU_MEM_UTIL |
0.85 |
cap ≤ 0.85 on GB10 |
MAX_NUM_BATCHED_TOKENS |
2048 |
≥ 1536 required with the vision tower on |
VLLM_SPARSE_INDEXER_MAX_LOGITS_MB |
256 |
must be set (empty → int('') crash after load) |
LANGUAGE_MODEL_ONLY |
0 |
vision on (set 1 for text-only) |
Sampling — official DeepSeek settings: temperature=1.0, top_p=0.95.
Thinking defaults on; set reasoning_effort to "low" / "high" / "max"
(or enable_thinking=false for no reasoning). API is OpenAI-compatible on
:8888, served model id DeepSeek-v4.1-Flash-EXL3.
Speculative decoding (DSpark) works on this abliterated build — measured ~45 % draft acceptance (mean accept length ~2), the same on harmful and harmless prompts, ~27 tok/s single-stream on 2× GB10. Vision, tools and long context are unchanged from the base pack.
Routed experts · Engram memory · CSA2 sparse attention · DSpark draft head · vision tower · router gates · RMSNorms · embeddings. Only the refusal circuit is removed.
This model has its safety guardrails removed. You are responsible for your inputs and outputs and for complying with all applicable law.
model-00038-of-00039.safetensors 8.1 GBmodel-00020-of-00039.safetensors 7.0 GBmodel-00019-of-00039.safetensors 7.0 GBmodel-00001-of-00039.safetensors 6.5 GBmodel-00039-of-00039.safetensors 5.8 GBmodel-00002-of-00039.safetensors 5.3 GBmodel-00015-of-00039.safetensors 5.3 GBmodel-00003-of-00039.safetensors 5.2 GBmodel-00009-of-00039.safetensors 5.2 GBmodel-00031-of-00039.safetensors 5.2 GBmodel-00035-of-00039.safetensors 5.2 GBmodel-00036-of-00039.safetensors 5.2 GBmodel-00037-of-00039.safetensors 5.2 GBmodel-00004-of-00039.safetensors 5.2 GBmodel-00005-of-00039.safetensors 5.2 GBmodel-00006-of-00039.safetensors 5.2 GBmodel-00007-of-00039.safetensors 5.2 GBmodel-00008-of-00039.safetensors 5.2 GBmodel-00010-of-00039.safetensors 5.2 GBmodel-00011-of-00039.safetensors 5.2 GBmodel-00029-of-00039.safetensors 5.2 GBmodel-00030-of-00039.safetensors 5.2 GBmodel-00032-of-00039.safetensors 5.2 GBmodel-00033-of-00039.safetensors 5.2 GBmodel-00034-of-00039.safetensors 5.2 GBmodel-00023-of-00039.safetensors 5.2 GBmodel-00027-of-00039.safetensors 5.2 GBmodel-00028-of-00039.safetensors 5.2 GBmodel-00012-of-00039.safetensors 5.2 GBmodel-00013-of-00039.safetensors 5.2 GBmodel-00014-of-00039.safetensors 5.2 GBmodel-00016-of-00039.safetensors 5.2 GBmodel-00017-of-00039.safetensors 5.2 GBmodel-00018-of-00039.safetensors 5.2 GBmodel-00022-of-00039.safetensors 5.2 GBmodel-00024-of-00039.safetensors 5.2 GBmodel-00025-of-00039.safetensors 5.2 GBmodel-00026-of-00039.safetensors 5.2 GBmodel-00021-of-00039.safetensors 3.5 GB有文件？提交种子。
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