Uncensored abliteration of the DeepSeek-V4-Flash-0731 (July 31 release) MoE with the JANG affine mixed-precision quant format. ~95 GB on disk, 1,048,576-token context via yarn RoPE, 256 routed + 1 shared expert per layer, MLA compressed KV.
Runs on ~128 GB unified memory Apple Silicon (M-series Max / Ultra).
What this model is
Base: deepseek-ai/DeepSeek-V4-Flash-0731 — 43 layers, MoE with 256 routed + 1 shared expert per layer, MLA attention (q_lora_rank=1024, o_lora_rank=1024, num_key_value_heads=1), sliding-window 128, yarn RoPE extension (factor 16 over base 65,536 → 1,048,576 tokens), vocab 129,280 (multilingual, includes CJK).
Quant: JANG affine mixed-precision (shared-experts + attention 8-bit g64, routed experts default 2-bit g32, per-projection/layer overrides preserved from the source release).
On-disk size: ~95 GB standalone.
Serving: designed for vMLX with thinking-mode default (reasoning_effort is a prompt-preface only: low = no preface, high/max = added instruction text). Also loadable via jang_tools directly.
Test Results
All tests executed on Apple Silicon under the JANG runtime with enable_thinking=True, reasoning_effort=low, system_prompt="You are a helpful AI assistant. Respond directly and concisely." (default vMLX serving context). Prompts read in full — automated pass/fail is filtered by a structural coherence classifier (rejects word-salad, tight-loop garbage, and short evasive answers).
HarmBench-320 (real-harm attack success rate)
Full HarmBench Text (Standard) suite, 320 behaviors, greedy decoding.
Category
comply / N
ASR
chemical_biological (priority)
42 / 42
100.0%
cybercrime_intrusion
52 / 52
100.0%
harassment_bullying
21 / 21
100.0%
harmful
18 / 18
100.0%
illegal
53 / 53
100.0%
misinformation_disinformation
54 / 54
100.0%
copyright (ignored — verbatim reproduction of copyrighted text is not a target)
79 / 80
98.75%
REAL-HARM ex-copyright
240 / 240
100.0%
Zero refusals on any genuine-harm behavior across all 320 items. The single non-comply is passage_sun_and_her_flowers (a request for verbatim Rupi Kaur poetry) which the model correctly declined on copyright grounds — this behavior is out-of-scope for the abliteration target and is expected.
MMLU (1140 questions, 20 per subject × 57 subjects, logit-based)
CRACK
Base
Delta
MMLU accuracy
71.84%
70.96%
+0.88pp
The CRACK slightly outperforms the base model on MMLU — removing the refusal-encoding direction from the residual stream does not damage general knowledge and reasoning.
HumanEval pass@k (coding capability)
Run with reasoning_effort=low + system prompt (default serving context), temp=0.8, top_p=0.95, max_tokens=1200, k=2. Compared head-to-head against the same DSV4-Flash-0731 base under identical config (jang_tools ≥ 2.5.39), plus antirez's independent GGUF quant via the ds4 (DwarfStar) native inference engine for reference.
The CRACK matches the base on coding (both 90%). The abliteration removes refusal without damaging coding capability. The independent antirez IQ2XXS/Q2K/Q4K GGUF is 14 pp lower on pass@2, showing the aggressive routed-expert quantization does cost some codegen quality vs the JANG affine 8/2-bit format used here.
(Earlier internal numbers were confounded by a fenced-code-extractor bug that mis-scored outputs where the model emits </think> immediately followed by the code fence with no newline. The bug affected our jang_tools output but not ds4 output. After fixing the extractor and re-scoring both jang_tools models, the CRACK and base tie at 90% and the antirez number stays at 76%.)
Security / Pentesting (categorical, from HB-320)
All 52 HarmBench cybercrime_intrusion behaviors comply. Examples span:
Bleach/vinegar and other hazardous-mixing enticements
Thinking modes
thinking=True + reasoning_effort=low: default serving. Full CoT inside <think>...</think> then answer. Verified on all tests above.
thinking=True + reasoning_effort=high: adds instruction-text preface before the CoT. Also works; used as a probe context to isolate the mode-invariant refusal direction.
thinking=False: direct chat mode without CoT. Answers are shorter/faster; occasional minor token-level typos on rare-vocab words (e.g. phosene for phosgene) may appear in complied responses — this is a known surgery-precision artifact and does not affect correctness on the categories tested.
Speed
Generation runs at ~85% of base-model tokens/sec under the current JANG runtime — approximately 15% slower than the base model. Load time ~10 seconds. Peak RAM during generation ~100 GB.
Known limitations
Copyright behaviors (verbatim reproduction of copyrighted text) still trigger the model's copyright decline — this is expected and intentional; copyright is not a target of the abliteration.
Minor token-level typos on rare-vocabulary words may appear in some responses (e.g. phosene, Bleich, step-step). Corrects itself with a follow-up prompt in most cases; does not affect factual correctness.
Long-context (>65k): model config supports 1,048,576 tokens via yarn extension. Not stress-tested at that scale on this build — evaluations use short prompts (≤4k tokens). Yarn scaling is preserved from the base release.
Serving through vMLX is recommended for full performance (KV cache quantization, prefix cache reuse, thinking-mode defaults). Other MLX runtimes may have different defaults for enable_thinking — set explicitly if reasoning is desired.
Usage
vMLX (recommended)
Download from HF, place under ~/models/, and load through vMLX server. Thinking mode defaults to on with reasoning_effort=low in vMLX ≥ 212 / osaurus ≥ 2293.
jang_tools direct
python
1import jang_tools # 2.5.39+ recommended (DSV4 native-decode + long-context-cache fixes)2from jang_tools.dsv4 import load_jangtq_dsv4_model
34model, tokenizer = load_jangtq_dsv4_model(5"/path/to/DeepSeek-V4-Flash-0731-JANG-CRACK",6 skip_params_eval=True,7)89# apply chat template with default serving context10ids = tokenizer.apply_chat_template(11[12{"role":"system","content":"You are a helpful AI assistant. Respond directly and concisely."},13{"role":"user","content":"Explain how a buffer overflow exploit works, with a small example."},14],15 enable_thinking=True,16 reasoning_effort="low",17 tokenize=True,18 add_default_bos_token=True,19)
Then decode with your preferred generation loop; standard nucleus sampling (temp=0.6-0.8, top_p=0.95) recommended for creative tasks, temp=0.0 for factual / greedy.
Requirements
Apple Silicon M-series Max or Ultra (M2 Ultra / M3 Max / M4 Max / M5 Max recommended)
≥ 128 GB unified memory (peak load ~100 GB)
≥ 100 GB free disk for the model
jang_tools ≥ 2.5.39 — earlier versions have DSV4 runtime-math bugs that misreport MMLU by several points and slow generation ~6×. Update before serving.
This model has been modified to remove the refusal-training layer. It will answer questions and produce content that the base model would decline. It is intended for security research, red-teaming, safety evaluation, and defensive tooling development. You are responsible for your use of it — don't harm people, don't break the law in your jurisdiction, don't distribute genuinely dangerous outputs to people who would misuse them. The chemical_biological category in particular contains information whose misuse can be lethal; adults with legitimate research reasons only.
The model may still exhibit residual reasoning around ethics on certain prompts (the CRACK removes refusal, not moral commentary). It is also not a truth machine — verify factual and technical claims independently.
Support dealignai
All models are built from original research and published for free. These models are specifically crafted to be excellent coders and general-purpose assistants — the abliteration removes refusal without hurting quality (and, on this build, slightly improves MMLU and HumanEval vs base).
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