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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
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:
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)
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
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
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We research and publish abliterated models to advance AI safety understanding.
See our research: Safety Generalization in Frontier MoE Models
Follow us: 𝕏 @dealignai
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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.