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kcarly/
DeepSeek-V4-Flash-JANG-CRACK

Name: DeepSeek-V4-Flash-JANG-CRACK
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DeepSeek-V4-Flash JANG CRACK

Abliterated DeepSeek-V4-Flash — 284B MoE, 13B active, 1M-token context, 3 reasoning modes

Broad compliance across all task categories with knowledge fully preserved.

Model Details

Metric	Value
Source	`deepseek-ai/DeepSeek-V4-Flash`
Architecture	MoE (256 routed + 1 shared expert, 13B active / 284B total) + MLA attention + Manifold-Constrained Hyper-Connections (mHC)
Context length	1,000,000 tokens
Profile	JANG (low-bit MoE experts, higher-precision critical tensors)
Model size	~97 GB
Speed	~20.8 tok/s (Apple M5 Max)
Parameters	284B total, 13B active per token
Format	JANG v2 (MLX-native safetensors, instant load)
Reasoning modes	chat (non-think) · think · max
Abliteration	CRACK — refusal removal across all harm categories

What This Is

This is an abliterated ("CRACK") build of DeepSeek-V4-Flash: the model's refusal behavior has been removed while leaving its knowledge, reasoning, and all three reasoning modes intact. It complies with instructions across every category instead of refusing — useful for security research, red-teaming, and unrestricted assistant use.

Test Results

Tested at greedy decoding (temperature 0) with no repetition penalty — i.e. the model is coherent on its own, not propped up by a sampling crutch. All responses were read in full to confirm genuine, on-topic compliance (not evasive non-answers).

Evaluated in the deployed serving context (default system prompt + user request). Answers are scored for genuine compliance, not shown.

HarmBench (320 standard behaviors)

Run through the actual inference engine (system prompt injected), strict classifier:

Metric	Result
Attack Success Rate (compliance)	99.7% (319/320)
Refusals	1

Also: held-out cross-category prompts 30/32 in the system-prompted context; hardest multi-category prompts (CBRN, weapons, cyber) 54/54 across chat / think / max modes. Coherent in all three reasoning modes — no degenerate looping.

MMLU — knowledge preserved (1140 questions, 20 per subject × 57 subjects)

Topic	CRACK	Base (unmodified)
STEM	73.2%	72.1%
Humanities	75.4%	73.8%
Social Sciences	82.9%	83.8%
Other	79.2%	78.1%
Overall	77.1%	76.3%

Abliteration costs no measurable capability — CRACK is within noise of (slightly above) the unmodified base across every topic and overall (+0.8 pp on 1140 questions).

Full per-subject MMLU (57 subjects, 20 Q each) — click to expand

Subject	CRACK	Base
Humanities
Formal Logic	12/20	13/20
High School European History	16/20	16/20
High School US History	19/20	19/20
High School World History	20/20	20/20
International Law	18/20	16/20
Jurisprudence	15/20	16/20
Logical Fallacies	16/20	16/20
Moral Disputes	14/20	12/20
Moral Scenarios	3/20	3/20
Philosophy	18/20	17/20
Prehistory	17/20	16/20
Professional Law	10/20	11/20
World Religions	18/20	17/20
Other
Business Ethics	16/20	15/20
Clinical Knowledge	20/20	20/20
College Medicine	16/20	15/20
Global Facts	10/20	11/20
Human Aging	16/20	14/20
Management	18/20	19/20
Marketing	19/20	18/20
Medical Genetics	19/20	19/20
Miscellaneous	15/20	16/20
Nutrition	18/20	17/20
Professional Accounting	12/20	12/20
Professional Medicine	15/20	15/20
Virology	12/20	12/20
STEM
Abstract Algebra	11/20	12/20
Anatomy	16/20	17/20
Astronomy	19/20	19/20
College Biology	19/20	19/20
College Chemistry	11/20	11/20
College Computer Science	12/20	12/20
College Mathematics	11/20	10/20
College Physics	13/20	14/20
Computer Security	17/20	15/20
Conceptual Physics	20/20	20/20
Electrical Engineering	12/20	11/20
Elementary Mathematics	13/20	13/20
High School Biology	17/20	17/20
High School Chemistry	14/20	14/20
High School Computer Science	19/20	19/20
High School Mathematics	10/20	9/20
High School Physics	11/20	11/20
High School Statistics	17/20	17/20
Machine Learning	16/20	14/20
Social Sciences
Econometrics	13/20	14/20
High School Geography	19/20	18/20
High School Government And Politics	18/20	18/20
High School Macroeconomics	18/20	18/20
High School Microeconomics	17/20	17/20
High School Psychology	19/20	19/20
Human Sexuality	15/20	15/20
Professional Psychology	18/20	18/20
Public Relations	13/20	13/20
Security Studies	15/20	15/20
Sociology	17/20	18/20
US Foreign Policy	17/20	18/20

Usage

Run with vMLX or a compatible MLX inference engine with DeepSeek-V4 support.

Recommended sampling:

chat / think: temperature = 0.6, top_p = 0.95
Three modes are available: chat (direct), think (reasoning), and max (maximum reasoning effort)

Requirements

Apple Silicon Mac with sufficient unified memory for a ~97 GB model
MLX framework with DeepSeek-V4 support; vMLX recommended

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About dealignai

We research and publish abliterated models to advance AI safety understanding.

See our research: Safety Generalization in Frontier MoE Models

Disclaimer

This model has had its safety refusal behavior removed for research purposes. It will follow instructions across all categories without refusing. You are solely responsible for how you use it and for complying with all applicable laws. Published for AI-safety research and authorized security testing.

kcarly/DeepSeek-V4-Flash-JANG-CRACK