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Justbackup/Llama-3.3-70B-Instruct-abliterated-i1-GGUF overview

About < quantize version: 2 < output tensor quantised: 1 < convert type: hf < vocab type: < tags: nicoboss weighted/imatrix quants of https://huggingface.co/hu…

transformersgguffacebookmetapytorchllamallama-3abliterateduncensoredenfritpthiesthdebase_model:huihui-ai/Llama-3.3-70B-Instruct-abliteratedbase_model:quantized:huihui-ai/Llama-3.3-70B-Instruct-abliteratedlicense:llama3.3endpoints_compatibleregion:usimatrixconversational

Runs locally from ~14.29 GB disk (16 GB VRAM class GPUs with llama.cpp / guIDE).

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Repository Files & Downloads

21 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Llama-3.3-70B-Instruct-abliterated.i1-IQ1_M.ggufGGUFIQ1_M15.60 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-IQ1_S.ggufGGUFIQ1_S14.29 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-IQ2_M.ggufGGUFIQ2_M22.46 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-IQ2_S.ggufGGUFIQ2_S20.71 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-IQ2_XS.ggufGGUFIQ2_XS19.69 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-IQ2_XXS.ggufGGUFIQ2_XXS17.79 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-IQ3_M.ggufGGUFIQ3_M29.74 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-IQ3_S.ggufGGUFIQ3_S28.79 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-IQ3_XS.ggufGGUFIQ3_XS27.29 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-IQ3_XXS.ggufGGUFIQ3_XXS25.58 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-IQ4_XS.ggufGGUFIQ4_XS35.30 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-Q2_K.ggufGGUFQ2_K24.56 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-Q2_K_S.ggufGGUFQ2_K_S22.79 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-Q3_K_L.ggufGGUFQ3_K_L34.59 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-Q3_K_M.ggufGGUFQ3_K_M31.91 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-Q3_K_S.ggufGGUFQ3_K_S28.79 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-Q4_0.ggufGGUFQ4_037.36 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-Q4_K_M.ggufGGUFQ4_K_M39.60 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-Q4_K_S.ggufGGUFQ4_K_S37.58 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-Q5_K_M.ggufGGUFQ5_K_M46.52 GBDownload
Llama-3.3-70B-Instruct-abliterated.i1-Q5_K_S.ggufGGUFQ5_K_S45.32 GBDownload

Model Details

Model IDJustbackup/Llama-3.3-70B-Instruct-abliterated-i1-GGUF
AuthorJustbackup
Pipeline
Licensellama3.3
Base modelhuihui-ai/Llama-3.3-70B-Instruct-abliterated
Last modified2026-08-19T16:19:40.000Z

Model README

---

base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated

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language:

  • en
  • fr
  • it
  • pt
  • hi
  • es
  • th
  • de

library_name: transformers

license: llama3.3

quantized_by: mradermacher

tags:

  • facebook
  • meta
  • pytorch
  • llama
  • llama-3
  • abliterated
  • uncensored

---

About

<!-- ### quantize_version: 2 -->

<!-- ### output_tensor_quantised: 1 -->

<!-- ### convert_type: hf -->

<!-- ### vocab_type: -->

<!-- ### tags: nicoboss -->

weighted/imatrix quants of https://huggingface.co/huihui-ai/Llama-3.3-70B-Instruct-abliterated

<!-- provided-files -->

static quants are available at https://huggingface.co/mradermacher/Llama-3.3-70B-Instruct-abliterated-GGUF

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 | i1-IQ1_S | 15.4 | for the desperate |

| GGUF | i1-IQ1_M | 16.9 | mostly desperate |

| GGUF | i1-IQ2_XXS | 19.2 | |

| GGUF | i1-IQ2_XS | 21.2 | |

| GGUF | i1-IQ2_S | 22.3 | |

| GGUF | i1-IQ2_M | 24.2 | |

| GGUF | i1-Q2_K_S | 24.6 | very low quality |

| GGUF | i1-Q2_K | 26.5 | IQ3_XXS probably better |

| GGUF | i1-IQ3_XXS | 27.6 | lower quality |

| GGUF | i1-IQ3_XS | 29.4 | |

| GGUF | i1-IQ3_S | 31.0 | beats Q3_K* |

| GGUF | i1-Q3_K_S | 31.0 | IQ3_XS probably better |

| GGUF | i1-IQ3_M | 32.0 | |

| GGUF | i1-Q3_K_M | 34.4 | IQ3_S probably better |

| GGUF | i1-Q3_K_L | 37.2 | IQ3_M probably better |

| GGUF | i1-IQ4_XS | 38.0 | |

| GGUF | i1-Q4_0 | 40.2 | fast, low quality |

| GGUF | i1-Q4_K_S | 40.4 | optimal size/speed/quality |

| GGUF | i1-Q4_K_M | 42.6 | fast, recommended |

| GGUF | i1-Q5_K_S | 48.8 | |

| GGUF | i1-Q5_K_M | 50.0 | |

| PART 1 PART 2 | i1-Q6_K | 58.0 | 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.

<!-- end -->

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