Instructions to use llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF",
	filename="Qwen3.5-27B-Writer-V2-mmproj-BF16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF:Q4_K_M

Use Docker

docker model run hf.co/llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF with Ollama:
```
ollama run hf.co/llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF:Q4_K_M
```

Unsloth Studio

How to use llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF to start chatting

How to use llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF with Docker Model Runner:
```
docker model run hf.co/llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF:Q4_K_M
```

Lemonade

How to use llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF-Q4_K_M

List all available models

lemonade list

🚨⚠️ I HAVE REACHED HUGGING FACE'S FREE STORAGE LIMIT ⚠️🚨

I can no longer upload new models unless I can cover the cost of additional storage.
I host 70+ free models as an independent contributor and this work is unpaid.
Without your support, no more new models can be uploaded.

🎉 Patreon (Monthly) | ☕ Ko-fi (One-time)

Every contribution goes directly toward Hugging Face storage fees to keep models free for everyone.

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

❤️ Support My Work

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

Platform	Link	What you get
🎉 Patreon	Monthly support	Priority model requests
☕ Ko-fi	One-time tip	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 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.

Downloads last month: 836

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

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

Base model

Qwen/Qwen3.5-27B

Finetuned

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

Finetuned

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

Quantized

(4)

this model

llmfan46
/

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

🚨⚠️ I HAVE REACHED HUGGING FACE'S FREE STORAGE LIMIT ⚠️🚨

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

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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

Targeted components

Performance

MMLU test results:

Quantizations

Vision Projector

Usage

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

Recommended Settings

Datasets

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

Datasets used to train llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic-GGUF