---

license: apache-2.0

base_model:

• llmfan46/Qwen3.5-9B-Nikusui-v1-Uncensored-Heretic-Native-MTP-Preserved

tags:

• JLENS
• Jacobian-Lens
• uncensored
• manual-abliteration
• conversational
• qwen3_5
• experimental
• NSFW
• very_very_naughty
• j-wash
• heretic
• uncensored
• decensored
• abliterated

---

<div style="background-color: #ff4444; color: white; padding: 20px; border-radius: 10px; text-align: center; margin: 20px 0;">

<h2 style="color: white; margin: 0 0 10px 0;">🚨⚠️ I HAVE REACHED HUGGING FACE'S FREE STORAGE LIMIT ⚠️🚨</h2>

<p style="font-size: 18px; margin: 0 0 15px 0;">I can no longer upload new models unless I can cover the cost of additional storage.<br>I host <b>70+ free models</b> as an independent contributor and this work is unpaid.<br><b>Without your support, no more new models can be uploaded.</b></p>

<p style="font-size: 20px; margin: 0;">

<a href="https://ko-fi.com/llmfan46" style="color: white; text-decoration: underline;">☕ Ko-fi</a>

</p>

<p style="font-size: 16px; margin: 10px 0 0 0;">Every contribution goes directly toward Hugging Face storage fees to keep models free for everyone.</p>

</div>

---

86% fewer refusals (11/100 Uncensored vs 96/100 Original) while preserving model quality (0.0067 KL divergence).

❤️ Support My Work

Creating these models takes significant time, work and compute. If you find them useful consider supporting me:

!image/png

| Platform | Link | What you get |

|----------|------|--------------|

| ☕ Ko-fi | Coffee Tips | My eternal gratitude |

Your help will motivate me and would go into further improving my workflow and coverings fees for storage, compute and may even help uncensoring bigger model with rental Cloud GPUs.

-----

GGUF quantizations of Qwen3.5-9B-Nikusui-v1-Uncensored-Heretic-Native-MTP-Preserved

This is a decensored version of extraltodeus/Qwen3.5-9B-Nikusui-v1, made using Heretic v1.4.0 with a variant of the Magnitude-Preserving Orthogonal Ablation (MPOA) method

Abliteration parameters

| Parameter | Value |

| :-------- | :---: |

| direction_index | 21.14 |

| attn.out_proj.max_weight | 1.90 |

| attn.out_proj.max_weight_position | 20.21 |

| attn.out_proj.min_weight | 1.38 |

| attn.out_proj.min_weight_distance | 20.35 |

| mlp.down_proj.max_weight | 1.96 |

| mlp.down_proj.max_weight_position | 19.78 |

| mlp.down_proj.min_weight | 1.22 |

| mlp.down_proj.min_weight_distance | 12.52 |

| attn.o_proj.max_weight | 1.83 |

| attn.o_proj.max_weight_position | 19.22 |

| attn.o_proj.min_weight | 0.47 |

| attn.o_proj.min_weight_distance | 23.77 |

Targeted components

* attn.out_proj

* mlp.down_proj

* attn.o_proj

Performance

| Metric | This model | Original model (Qwen3.5-9B-Nikusui-v1) |

| :----- | :--------: | :---------------------------: |

| KL divergence | <span style="color:darkgoldenrod">0.0067</span> | 0 (by definition) |

| Refusals | ✅ <span style="color:darkgreen">11/100</span> | ❌ <span style="color:blue">96/100</span> |

MMLU test results:

<span style="color:blue">Original:</span>

============================================================

• Total questions: 7021

• Correct: 5426

• Accuracy: 0.7728 (77.28%)

• Parse failures: 0

============================================================

Tested subject scores:

• professional_law: 0.6140 (482/785)
• moral_scenarios: 0.4796 (212/442)
• miscellaneous: 0.8825 (338/383)
• professional_psychology: 0.8259 (261/316)
• high_school_psychology: 0.9519 (257/270)
• high_school_macroeconomics: 0.8426 (166/197)
• elementary_mathematics: 0.7011 (129/184)
• moral_disputes: 0.7989 (139/174)
• prehistory: 0.8547 (147/172)
• philosophy: 0.7862 (125/159)
• high_school_biology: 0.9539 (145/152)
• professional_accounting: 0.6573 (94/143)
• clinical_knowledge: 0.8143 (114/140)
• high_school_microeconomics: 0.9338 (127/136)
• nutrition: 0.8296 (112/135)
• professional_medicine: 0.8731 (117/134)
• conceptual_physics: 0.8359 (107/128)
• high_school_mathematics: 0.5433 (69/127)
• human_aging: 0.7759 (90/116)
• security_studies: 0.8750 (98/112)
• high_school_statistics: 0.7568 (84/111)
• marketing: 0.9450 (103/109)
• high_school_world_history: 0.9245 (98/106)
• sociology: 0.9126 (94/103)
• high_school_government_and_politics: 0.9604 (97/101)
• high_school_geography: 0.9293 (92/99)
• high_school_chemistry: 0.7629 (74/97)
• high_school_us_history: 0.9368 (89/95)
• virology: 0.5169 (46/89)
• college_medicine: 0.7955 (70/88)
• world_religions: 0.8750 (77/88)
• high_school_physics: 0.6548 (55/84)
• electrical_engineering: 0.7160 (58/81)
• astronomy: 0.9241 (73/79)
• logical_fallacies: 0.8289 (63/76)
• high_school_european_history: 0.8767 (64/73)
• anatomy: 0.8028 (57/71)
• college_biology: 0.9219 (59/64)
• human_sexuality: 0.8125 (52/64)
• formal_logic: 0.6406 (41/64)
• public_relations: 0.7377 (45/61)
• international_law: 0.9000 (54/60)
• college_physics: 0.6316 (36/57)
• college_mathematics: 0.5455 (30/55)
• econometrics: 0.7037 (38/54)
• jurisprudence: 0.8679 (46/53)
• high_school_computer_science: 0.8269 (43/52)
• machine_learning: 0.6346 (33/52)
• medical_genetics: 0.9020 (46/51)
• global_facts: 0.4510 (23/51)
• management: 0.9400 (47/50)
• us_foreign_policy: 0.9400 (47/50)
• college_chemistry: 0.5532 (26/47)
• abstract_algebra: 0.6383 (30/47)
• business_ethics: 0.6957 (32/46)
• college_computer_science: 0.8444 (38/45)
• computer_security: 0.8605 (37/43)

<span style="color:darkgreen">Heretic:</span>

============================================================

• Total questions: 7021

• Correct: 5413

• Accuracy: 0.7710 (77.10%)

• Parse failures: 0

============================================================

Tested subject scores:

• professional_law: 0.6089 (478/785)
• moral_scenarios: 0.4774 (211/442)
• miscellaneous: 0.8877 (340/383)
• professional_psychology: 0.8291 (262/316)
• high_school_psychology: 0.9519 (257/270)
• high_school_macroeconomics: 0.8528 (168/197)
• elementary_mathematics: 0.7011 (129/184)
• moral_disputes: 0.7989 (139/174)
• prehistory: 0.8488 (146/172)
• philosophy: 0.7673 (122/159)
• high_school_biology: 0.9539 (145/152)
• professional_accounting: 0.6573 (94/143)
• clinical_knowledge: 0.8143 (114/140)
• high_school_microeconomics: 0.9338 (127/136)
• nutrition: 0.8370 (113/135)
• professional_medicine: 0.8582 (115/134)
• conceptual_physics: 0.8359 (107/128)
• high_school_mathematics: 0.5512 (70/127)
• human_aging: 0.7759 (90/116)
• security_studies: 0.8571 (96/112)
• high_school_statistics: 0.7387 (82/111)
• marketing: 0.9450 (103/109)
• high_school_world_history: 0.9151 (97/106)
• sociology: 0.9223 (95/103)
• high_school_government_and_politics: 0.9505 (96/101)
• high_school_geography: 0.9394 (93/99)
• high_school_chemistry: 0.7629 (74/97)
• high_school_us_history: 0.9368 (89/95)
• virology: 0.5169 (46/89)
• college_medicine: 0.8068 (71/88)
• world_religions: 0.8864 (78/88)
• high_school_physics: 0.6548 (55/84)
• electrical_engineering: 0.7160 (58/81)
• astronomy: 0.9241 (73/79)
• logical_fallacies: 0.8421 (64/76)
• high_school_european_history: 0.8767 (64/73)
• anatomy: 0.8028 (57/71)
• college_biology: 0.9375 (60/64)
• human_sexuality: 0.8125 (52/64)
• formal_logic: 0.6250 (40/64)
• public_relations: 0.7049 (43/61)
• international_law: 0.8667 (52/60)
• college_physics: 0.6316 (36/57)
• college_mathematics: 0.5455 (30/55)
• econometrics: 0.6667 (36/54)
• jurisprudence: 0.8679 (46/53)
• high_school_computer_science: 0.8269 (43/52)
• machine_learning: 0.6346 (33/52)
• medical_genetics: 0.8824 (45/51)
• global_facts: 0.4314 (22/51)
• management: 0.9400 (47/50)
• us_foreign_policy: 0.9600 (48/50)
• college_chemistry: 0.5319 (25/47)
• abstract_algebra: 0.6596 (31/47)
• business_ethics: 0.6957 (32/46)
• college_computer_science: 0.8444 (38/45)
• computer_security: 0.8372 (36/43)

MMLU - Massive Multitask Language Understanding, multiple-choice questions across 57 subjects (math, history, law, medicine, etc.).

-----

Quantizations

For the K-quants below, small SSM tensors are kept at higher precision where useful.

-Q6_K quants keep ssm_alpha, ssm_beta, and ssm_out as Q8_0.

-Q5_K and Q4_K quants keep ssm_alpha, ssm_beta as Q8_0 and and ssm_out as Q6_K.

This helps preserve the hybrid/SSM blocks with a small file-size increase.

| Filename | Quant | Description |

|----------|-------|-------------|

| Qwen3.5-9B-Nikusui-v1-Uncensored-Heretic-Native-MTP-Preserved-BF16.gguf | BF16 | Full precision |

| Qwen3.5-9B-Nikusui-v1-Uncensored-Heretic-Native-MTP-Preserved-Q8_0.gguf | Q8_0 | Near-lossless, recommended |

| Qwen3.5-9B-Nikusui-v1-Uncensored-Heretic-Native-MTP-Preserved-Q6_K.gguf | Q6_K | Excellent quality |

| Qwen3.5-9B-Nikusui-v1-Uncensored-Heretic-Native-MTP-Preserved-Q5_K_M.gguf | Q5_K_M | Good balance |

| Qwen3.5-9B-Nikusui-v1-Uncensored-Heretic-Native-MTP-Preserved-Q5_K_S.gguf | Q5_K_S | Smaller Q5 |

| Qwen3.5-9B-Nikusui-v1-Uncensored-Heretic-Native-MTP-Preserved-Q4_K_M.gguf | Q4_K_M | Good for limited VRAM |

Vision Projector

| Filename | Quant | Description |

|----------|-------|-------------|

| Qwen3.5-9B-Nikusui-v1-Uncensored-Heretic-Native-MTP-Preserved-mmproj-BF16.gguf | BF16 | Native precision |

A Vision Projector File is Required for vision/multimodal capabilities. Use alongside any quantization above.

Usage

Works with llama.cpp, LM Studio, Ollama, and other GGUF-compatible tools.

-----

Nikusui - v1

!nikusui2

Nikusui is a manually abliterated Qwen3.5-9B-Base model using a custom tool in the making and based on Anthropic's Jacobian-Lens.

The tool is currently still a work in progress but I intend to share it. I just need to sleep after spend three days on this. 😴

It will allow to save a model while retaining the effects of any modification made in the J-Space. Suppression and replacement.

Nikusui-v1 is the very first created by this tool and is a fully working proof of concept.

~I haven't decided a name for the tool yet 🤭~ J-Wash it is!

If you're curious : the file "edit_meta.json" contains the settings I used in my tool to edit the base model and will give you more clues about why it behaves like it does.

All modifications were made on Qwen/Qwen3.5-9B-Base directly.

THIS MODEL WILL SPONTANEOUSLY PRODUCE HARMFUL CONTENT ! Or at least offer a spanking.

In the name of science.