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Azerty76/Llama-3.3-70B-Instruct-abliterated-i1-GGUF

Azerty76 Llama 70B GGUF second-order 131K ctx
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Response includes
  • classification m8
  • files 26
  • benchmarks 11 entries
  • hub_downloads_all_time 1,437
  • author_summary 3 models
  • readme_text full
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Abliteration classifier · v1.0.0
M8
Primary method

Repackaging (quantization)

Applied on top of direct removal inherited from the base model.
Confidence
MEDIUM
Inherited from base model
Why this label 3 signals
Method inferred from partial signals - repository name, related files, or tag patterns. Producer identity not confirmed; label may sharpen or shift as we gather more evidence.
  • 'abliterated' in name/tags
  • is_gguf=1
  • assume M1 (base ablation) + M8 (GGUF quant) - default when producer unknown
Refusal direction extraction

No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

What is a refusal direction? →
Downloads · lifetime
1K
444 last 30d - stable
Likes
0
Model age
6mo ago
created 2026-03-25
Downloads over time
Now1.5K→from255↑500%
1916801.2K1.7K255 on Mar 251.5K on Oct 11MarAprMayJunJulAugSepOct
Mar 25 → Oct 11 · 68 snapshots · spans 200 days

Benchmarks

Benchmark Score Source
Entertainment 3.2 UGI
Hazardous 3.5 UGI
Natural Intelligence 33.54 UGI
Political lean -20.3% UGI
Sensitive-Info 32.66 UGI
SocPol 3.1 UGI
UGI 48.44 UGI
Willingness (10) 8 UGI
W10-Adherence 8 UGI
W10-Direct 8 UGI
Writing 31.76 UGI

Genealogy 0 direct forks

Full fork graph →

This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Metadata

License
llama3.3
Languages
en fr it pt hi es th de
Quantizations
IQ1 IQ2 IQ3 IQ4 Q2_K Q3_K Q4 Q4_K Q5_K
Tags
transformers gguf facebook meta pytorch llama llama-3 abliterated uncensored en fr it

Related

Total size
606 GB
Files
26
Quantizations
10
Registered
2026-08-22 13:56
Last updated on HF
2026-03-25 18:37

Files by quantization

Q5_K 2 files 91.8 GB
Llama-3.3-70B-Instruct-abliterated.i1-Q5_K_M.gguf 46.5 GB 4dfc7c22 download
Llama-3.3-70B-Instruct-abliterated.i1-Q5_K_S.gguf 45.3 GB 8b49babf download
Q4_K 2 files 77.2 GB
Llama-3.3-70B-Instruct-abliterated.i1-Q4_K_M.gguf 39.6 GB 4a524f8d download
Llama-3.3-70B-Instruct-abliterated.i1-Q4_K_S.gguf 37.6 GB 3eb703cf download
Q4 1 file 37.4 GB
Llama-3.3-70B-Instruct-abliterated.i1-Q4_0.gguf 37.4 GB 984cf813 download
IQ4 1 file 35.3 GB
Llama-3.3-70B-Instruct-abliterated.i1-IQ4_XS.gguf 35.3 GB fd3bb5cc download
Q3_K 3 files 95.3 GB
Llama-3.3-70B-Instruct-abliterated.i1-Q3_K_L.gguf 34.6 GB ca37cf1e download
Llama-3.3-70B-Instruct-abliterated.i1-Q3_K_M.gguf 31.9 GB 458aedaf download
Llama-3.3-70B-Instruct-abliterated.i1-Q3_K_S.gguf 28.8 GB 2e1f56f6 download
IQ3 4 files 111 GB
Llama-3.3-70B-Instruct-abliterated.i1-IQ3_M.gguf 29.7 GB 3f2544b2 download
Llama-3.3-70B-Instruct-abliterated.i1-IQ3_S.gguf 28.8 GB 4c1606a7 download
Llama-3.3-70B-Instruct-abliterated.i1-IQ3_XS.gguf 27.3 GB eb7f3281 download
Llama-3.3-70B-Instruct-abliterated.i1-IQ3_XXS.gguf 25.6 GB fe79b116 download
Q2_K 2 files 47.4 GB
Llama-3.3-70B-Instruct-abliterated.i1-Q2_K.gguf 24.6 GB be29d3f4 download
Llama-3.3-70B-Instruct-abliterated.i1-Q2_K_S.gguf 22.8 GB f5e08326 download
IQ2 4 files 80.7 GB
Llama-3.3-70B-Instruct-abliterated.i1-IQ2_M.gguf 22.5 GB 92e91389 download
Llama-3.3-70B-Instruct-abliterated.i1-IQ2_S.gguf 20.7 GB 2e8f7f82 download
Llama-3.3-70B-Instruct-abliterated.i1-IQ2_XS.gguf 19.7 GB ee09355e download
Llama-3.3-70B-Instruct-abliterated.i1-IQ2_XXS.gguf 17.8 GB b3c24c2a download
IQ1 2 files 29.9 GB
Llama-3.3-70B-Instruct-abliterated.i1-IQ1_M.gguf 15.6 GB 48bb181d download
Llama-3.3-70B-Instruct-abliterated.i1-IQ1_S.gguf 14.3 GB 27ad59ce download
Auxiliary files 5 files 53.9 GB
Llama-3.3-70B-Instruct-abliterated.i1-Q6_K.gguf.part1of2 27.0 GB cef5c604 download
Llama-3.3-70B-Instruct-abliterated.i1-Q6_K.gguf.part2of2 26.9 GB 151eb072 download
imatrix.dat 23.8 MB 707178d5 download
README.md 20.5 KB 45d3db46 download
.gitattributes 3.47 KB 6848faf8 download

README current version from Hugging Face


base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated
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3.3: [email protected] "
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

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

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
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.

README history 1 version

The author's README evolved over time. Click a version to see its content at that point.

  1. 2026-03-25Duplicate from mradermacher/Llama-3.3-70B-Instruct-abliterated-i1-GGUFeffe75020.5 KB
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