mradermacher/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-i1-GGUF overview

---

base_model: AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16

language:

• en
• zh
• multilingual

library_name: transformers

license: apache-2.0

mradermacher:

readme_rev: 1

quantized_by: mradermacher

tags:

• 27b
• a100
• aarch64
• abliterated
• abliterix
• aeon
• aeon-7
• agentic
• arm64
• bf16
• bfloat16
• blackwell
• chat
• chunked-prefill
• coding
• conversational
• dgx-spark
• english
• fernflower-ssm-repair
• fine-tuning
• function-calling
• gated-deltanet
• gb10
• gdn
• gpu
• grace-blackwell
• h100
• hybrid
• hybrid-attention
• instruct
• linear-attention
• long-context
• mamba
• multi-gpu
• multimodal
• openai-api
• openai-compatible
• pre-blackwell
• prefix-caching
• production-ready
• qwen
• qwen3
• qwen3.5
• qwen3.6
• reasoning
• refusal-removed
• safetensors
• sm_121a
• sm_80
• sm_90
• thinking
• tool-calling
• uncensored
• unfiltered
• vision
• vision-language
• vllm

---

About

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<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->

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weighted/imatrix quants of https://huggingface.co/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16

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For a convenient overview and download list, visit our model page for this model.

static quants are available at https://huggingface.co/mradermacher/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-GGUF

This is a vision model - mmproj files (if any) will be in the static repository.

Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's

READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for

more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

| Link | Type | Size/GB | Notes |

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

| GGUF | imatrix | 0.1 | imatrix file (for creating your own quants) |

| GGUF | i1-IQ1_S | 7.2 | for the desperate |

| GGUF | i1-IQ1_M | 7.7 | mostly desperate |

| GGUF | i1-IQ2_XXS | 8.5 | |

| GGUF | i1-IQ2_XS | 9.2 | |

| GGUF | i1-IQ2_S | 9.5 | |

| GGUF | i1-IQ2_M | 10.1 | |

| GGUF | i1-Q2_K_S | 10.3 | very low quality |

| GGUF | i1-Q2_K | 10.8 | IQ3_XXS probably better |

| GGUF | i1-IQ3_XXS | 11.3 | lower quality |

| GGUF | i1-IQ3_XS | 12.1 | |

| GGUF | i1-Q3_K_S | 12.2 | IQ3_XS probably better |

| GGUF | i1-IQ3_S | 12.5 | beats Q3_K* |

| GGUF | i1-IQ3_M | 12.7 | |

| GGUF | i1-Q3_K_M | 13.4 | IQ3_S probably better |

| GGUF | i1-Q3_K_L | 14.4 | IQ3_M probably better |

| GGUF | i1-IQ4_XS | 15.2 | |

| GGUF | i1-Q4_0 | 15.6 | fast, low quality |

| GGUF | i1-Q4_K_S | 15.7 | optimal size/speed/quality |

| GGUF | i1-Q4_K_M | 16.6 | fast, recommended |

| GGUF | i1-Q4_1 | 17.2 | |

| GGUF | i1-Q5_K_S | 18.8 | |

| GGUF | i1-Q5_K_M | 19.3 | |

| GGUF | i1-Q6_K | 22.2 | practically like static Q6_K |

Here is a handy graph by ikawrakow comparing some lower-quality quant

types (lower is better):

!image.png

And here are Artefact2's thoughts on the matter:

https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to

questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting

me use its servers and providing upgrades to my workstation to enable

this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

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