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91% fewer refusals (8/100 Uncensored vs 93/100 Original) while preserving model quality (0.0274 KL divergence).

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

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GGUF quantizations of llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic.

This model is great for creative writing and translation, the original base model writing and translations feels a litle stiff which might not really read very nicely some times, Qwen3.5-27B-Writer-V2-uncensored-heretic aims to fix this issue and improve the writing quality of Qwen3.5-27B.

This is a decensored version of ConicCat/Qwen3.5-27B-Writer-V2, made using Heretic v1.2.0 with the Arbitrary-Rank Ablation (ARA) method

Abliteration parameters

Parameter	Value
start_layer_index	31
end_layer_index	56
preserve_good_behavior_weight	0.4059
steer_bad_behavior_weight	0.0001
overcorrect_relative_weight	1.1869
neighbor_count	10

Targeted components

attn.o_proj
attn.out_proj

Performance

Metric	This model	Original model (ConicCat/Qwen3.5-27B-Writer-V2)
KL divergence	0.0274	0 (by definition)
Refusals	✅ 8/100	❌ 93/100

Lower refusals indicate fewer content restrictions, while lower KL divergence indicates more closeness to the original model's baseline. Higher refusals cause more rejections, objections, pushbacks, lecturing, censorship, softening and deflections.

MMLU test results:

Original:

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
mmlu	2	none		acc	↑	0.8562	±	0.0028
- humanities	2	none		acc	↑	0.8047	±	0.0056
- formal_logic	1	none	0	acc	↑	0.7302	±	0.0397
- high_school_european_history	1	none	0	acc	↑	0.9030	±	0.0231
- high_school_us_history	1	none	0	acc	↑	0.9412	±	0.0165
- high_school_world_history	1	none	0	acc	↑	0.9409	±	0.0153
- international_law	1	none	0	acc	↑	0.9256	±	0.0240
- jurisprudence	1	none	0	acc	↑	0.9074	±	0.0280
- logical_fallacies	1	none	0	acc	↑	0.9202	±	0.0213
- moral_disputes	1	none	0	acc	↑	0.8584	±	0.0188
- moral_scenarios	1	none	0	acc	↑	0.7352	±	0.0148
- philosophy	1	none	0	acc	↑	0.8842	±	0.0182
- prehistory	1	none	0	acc	↑	0.9167	±	0.0154
- professional_law	1	none	0	acc	↑	0.7080	±	0.0116
- world_religions	1	none	0	acc	↑	0.9181	±	0.0210
- other	2	none		acc	↑	0.8735	±	0.0057
- business_ethics	1	none	0	acc	↑	0.8300	±	0.0378
- clinical_knowledge	1	none	0	acc	↑	0.8868	±	0.0195
- college_medicine	1	none	0	acc	↑	0.8382	±	0.0281
- global_facts	1	none	0	acc	↑	0.6200	±	0.0488
- human_aging	1	none	0	acc	↑	0.8430	±	0.0244
- management	1	none	0	acc	↑	0.8738	±	0.0329
- marketing	1	none	0	acc	↑	0.9530	±	0.0139
- medical_genetics	1	none	0	acc	↑	0.9700	±	0.0171
- miscellaneous	1	none	0	acc	↑	0.9387	±	0.0086
- nutrition	1	none	0	acc	↑	0.9020	±	0.0170
- professional_accounting	1	none	0	acc	↑	0.8014	±	0.0238
- professional_medicine	1	none	0	acc	↑	0.9522	±	0.0130
- virology	1	none	0	acc	↑	0.5723	±	0.0385
- social sciences	2	none		acc	↑	0.9162	±	0.0049
- econometrics	1	none	0	acc	↑	0.8158	±	0.0365
- high_school_geography	1	none	0	acc	↑	0.9596	±	0.0140
- high_school_government_and_politics	1	none	0	acc	↑	0.9896	±	0.0073
- high_school_macroeconomics	1	none	0	acc	↑	0.9282	±	0.0131
- high_school_microeconomics	1	none	0	acc	↑	0.9664	±	0.0117
- high_school_psychology	1	none	0	acc	↑	0.9541	±	0.0090
- human_sexuality	1	none	0	acc	↑	0.9160	±	0.0243
- professional_psychology	1	none	0	acc	↑	0.8725	±	0.0135
- public_relations	1	none	0	acc	↑	0.7636	±	0.0407
- security_studies	1	none	0	acc	↑	0.8449	±	0.0232
- sociology	1	none	0	acc	↑	0.9652	±	0.0130
- us_foreign_policy	1	none	0	acc	↑	0.9400	±	0.0239
- stem	2	none		acc	↑	0.8576	±	0.0060
- abstract_algebra	1	none	0	acc	↑	0.8000	±	0.0402
- anatomy	1	none	0	acc	↑	0.8296	±	0.0325
- astronomy	1	none	0	acc	↑	0.9671	±	0.0145
- college_biology	1	none	0	acc	↑	0.9792	±	0.0119
- college_chemistry	1	none	0	acc	↑	0.6800	±	0.0469
- college_computer_science	1	none	0	acc	↑	0.8300	±	0.0378
- college_mathematics	1	none	0	acc	↑	0.6800	±	0.0469
- college_physics	1	none	0	acc	↑	0.8235	±	0.0379
- computer_security	1	none	0	acc	↑	0.8700	±	0.0338
- conceptual_physics	1	none	0	acc	↑	0.9404	±	0.0155
- electrical_engineering	1	none	0	acc	↑	0.8276	±	0.0315
- elementary_mathematics	1	none	0	acc	↑	0.9101	±	0.0147
- high_school_biology	1	none	0	acc	↑	0.9516	±	0.0122
- high_school_chemistry	1	none	0	acc	↑	0.8522	±	0.0250
- high_school_computer_science	1	none	0	acc	↑	0.9300	±	0.0256
- high_school_mathematics	1	none	0	acc	↑	0.6741	±	0.0286
- high_school_physics	1	none	0	acc	↑	0.8609	±	0.0283
- high_school_statistics	1	none	0	acc	↑	0.8704	±	0.0229
- machine_learning	1	none	0	acc	↑	0.7857	±	0.0389

Groups	Version	Filter	Metric		Value		Stderr
mmlu	2	none	acc	↑	0.8562	±	0.0028
- humanities	2	none	acc	↑	0.8047	±	0.0056
- other	2	none	acc	↑	0.8735	±	0.0057
- social sciences	2	none	acc	↑	0.9162	±	0.0049
- stem	2	none	acc	↑	0.8576	±	0.0060

Heretic:

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
mmlu	2	none		acc	↑	0.8469	±	0.0029
- humanities	2	none		acc	↑	0.7858	±	0.0058
- formal_logic	1	none	0	acc	↑	0.7302	±	0.0397
- high_school_european_history	1	none	0	acc	↑	0.8970	±	0.0237
- high_school_us_history	1	none	0	acc	↑	0.9412	±	0.0165
- high_school_world_history	1	none	0	acc	↑	0.9367	±	0.0158
- international_law	1	none	0	acc	↑	0.9256	±	0.0240
- jurisprudence	1	none	0	acc	↑	0.9167	±	0.0267
- logical_fallacies	1	none	0	acc	↑	0.8957	±	0.0240
- moral_disputes	1	none	0	acc	↑	0.8526	±	0.0191
- moral_scenarios	1	none	0	acc	↑	0.6458	±	0.0160
- philosophy	1	none	0	acc	↑	0.8810	±	0.0184
- prehistory	1	none	0	acc	↑	0.9043	±	0.0164
- professional_law	1	none	0	acc	↑	0.7086	±	0.0116
- world_religions	1	none	0	acc	↑	0.9298	±	0.0196
- other	2	none		acc	↑	0.8725	±	0.0057
- business_ethics	1	none	0	acc	↑	0.8200	±	0.0386
- clinical_knowledge	1	none	0	acc	↑	0.9057	±	0.0180
- college_medicine	1	none	0	acc	↑	0.8613	±	0.0264
- global_facts	1	none	0	acc	↑	0.5600	±	0.0499
- human_aging	1	none	0	acc	↑	0.8341	±	0.0250
- management	1	none	0	acc	↑	0.9223	±	0.0265
- marketing	1	none	0	acc	↑	0.9573	±	0.0133
- medical_genetics	1	none	0	acc	↑	0.9700	±	0.0171
- miscellaneous	1	none	0	acc	↑	0.9425	±	0.0083
- nutrition	1	none	0	acc	↑	0.9020	±	0.0170
- professional_accounting	1	none	0	acc	↑	0.7766	±	0.0248
- professional_medicine	1	none	0	acc	↑	0.9338	±	0.0151
- virology	1	none	0	acc	↑	0.5723	±	0.0385
- social sciences	2	none		acc	↑	0.9110	±	0.0050
- econometrics	1	none	0	acc	↑	0.8070	±	0.0371
- high_school_geography	1	none	0	acc	↑	0.9495	±	0.0156
- high_school_government_and_politics	1	none	0	acc	↑	0.9845	±	0.0089
- high_school_macroeconomics	1	none	0	acc	↑	0.9205	±	0.0137
- high_school_microeconomics	1	none	0	acc	↑	0.9664	±	0.0117
- high_school_psychology	1	none	0	acc	↑	0.9486	±	0.0095
- human_sexuality	1	none	0	acc	↑	0.9084	±	0.0253
- professional_psychology	1	none	0	acc	↑	0.8742	±	0.0134
- public_relations	1	none	0	acc	↑	0.7727	±	0.0401
- security_studies	1	none	0	acc	↑	0.8204	±	0.0246
- sociology	1	none	0	acc	↑	0.9602	±	0.0138
- us_foreign_policy	1	none	0	acc	↑	0.9400	±	0.0239
- stem	2	none		acc	↑	0.8503	±	0.0061
- abstract_algebra	1	none	0	acc	↑	0.7100	±	0.0456
- anatomy	1	none	0	acc	↑	0.8444	±	0.0313
- astronomy	1	none	0	acc	↑	0.9605	±	0.0158
- college_biology	1	none	0	acc	↑	0.9722	±	0.0137
- college_chemistry	1	none	0	acc	↑	0.6400	±	0.0482
- college_computer_science	1	none	0	acc	↑	0.8300	±	0.0378
- college_mathematics	1	none	0	acc	↑	0.7100	±	0.0456
- college_physics	1	none	0	acc	↑	0.8529	±	0.0352
- computer_security	1	none	0	acc	↑	0.8600	±	0.0349
- conceptual_physics	1	none	0	acc	↑	0.9362	±	0.0160
- electrical_engineering	1	none	0	acc	↑	0.8276	±	0.0315
- elementary_mathematics	1	none	0	acc	↑	0.9074	±	0.0149
- high_school_biology	1	none	0	acc	↑	0.9387	±	0.0136
- high_school_chemistry	1	none	0	acc	↑	0.8473	±	0.0253
- high_school_computer_science	1	none	0	acc	↑	0.9200	±	0.0273
- high_school_mathematics	1	none	0	acc	↑	0.6630	±	0.0288
- high_school_physics	1	none	0	acc	↑	0.8411	±	0.0299
- high_school_statistics	1	none	0	acc	↑	0.8704	±	0.0229
- machine_learning	1	none	0	acc	↑	0.7768	±	0.0395

Groups	Version	Filter	Metric		Value		Stderr
mmlu	2	none	acc	↑	0.8469	±	0.0029
- humanities	2	none	acc	↑	0.7858	±	0.0058
- other	2	none	acc	↑	0.8725	±	0.0057
- social sciences	2	none	acc	↑	0.9110	±	0.0050
- stem	2	none	acc	↑	0.8503	±	0.0061

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

Quantizations

Filename	Quant	Description
Qwen3.5-27B-Writer-V2-uncensored-heretic-BF16.gguf	BF16	Full precision
Qwen3.5-27B-Writer-V2-uncensored-heretic-Q8_0.gguf	Q8_0	Near-lossless, recommended
Qwen3.5-27B-Writer-V2-uncensored-heretic-Q6_K.gguf	Q6_K	Excellent quality
Qwen3.5-27B-Writer-V2-uncensored-heretic-Q5_K_M.gguf	Q5_K_M	Good balance
Qwen3.5-27B-Writer-V2-uncensored-heretic-Q5_K_SQwen3.5-27B-ultra-uncensored-heretic-v2-v2-Q5_K_S.gguf	Q5_K_S	Smaller Q5
Qwen3.5-27B-Writer-V2-uncensored-heretic-Q4_K_M.gguf	Q4_K_M	Good for limited VRAM
Qwen3.5-27B-Writer-V2-uncensored-heretic-Q4_K_S.gguf	Q4_K_S	Smaller Q4
Qwen3.5-27B-Writer-V2-uncensored-heretic-Q3_K_L.gguf	Q3_K_L	Low VRAM, decent quality
Qwen3.5-27B-Writer-V2-uncensored-heretic-Q3_K_M.gguf	Q3_K_M	Low VRAM, smaller

Vision Projector

Filename	Quant	Description
Qwen3.5-27B-Writer-V2-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.

ConicCat/Qwen3.5-27B-Writer-V2

A tentative second version. Hopefully, it's better.

A writing & roleplay finetune of Qwen3.5 27B. The primary emphasis is on writing quality as it strongly generalizes across both domains.

The basic idea is to use a curriculum learning setup to overcome the lack of high quality roleplay data by first training on lower quality roleplay data, then training on higher quality writing data. Starting from ConicCat/Qwen3.5-Antirep-27B, the model was trained on a roughly equal mixture of instruct / roleplay / writing data for three epochs. The model was then trained for eleven epochs on a smaller dataset of book chunks.

Recommended Settings

Chatml template with <think>\n\n</think>\n prefill or <think>\n prefill. Should think less!
temperature = 0.7
top_p = 0.95
A moderate dry penalty of ~ 0.4-0.8 should work well.
For quants, Q4_K_M runs well with ~100k context on 24GB Vram
IQ4_XS should fit on 16GB Vram with about 20-24k context with the vulkan backend, although it's pretty tight and may require some fiddling around with open programs e.t.c.

Datasets

ConicCat/AntiRep to mitigate repetitition.
internlm/Condor-SFT-20K for instruct; even though instruct capabilities are not the primary focus, adding some instruct data helps mitigate forgetting and maintains general intellect and instruction following capabilites.
ConicCat/Gutenberg-SFT. A reformatted version of the original Gutenberg DPO dataset by jondurbin for SFT with some slight augmentation to address many of the samples being overly long.
ConicCat/MiniC2_V3.2. The venerable C2, with cleaned and reformatted system prompts, and all user / assistant turns replaced by V3.2.
A dataset of backtranslated books. Unfortunately, I am unable to release this set as all of the data is under copyright.

llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF

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