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Iambackup/Llama-4-Scout-17B-16E-Instruct-abliterated-v2-i1-GGUF

Iambackup Llama 17B GGUF second-order 10.5M ctx
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  • classification m8
  • files 3
  • hub_downloads_all_time 704
  • author_summary 36 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.

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Downloads · lifetime
704
69 last 30d - cooling
Likes
0
Model age
4mo ago
created 2026-06-11
Downloads over time
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927153379543 on Jun 10727 on Oct 11JunJulAugSepOct
Jun 10 → Oct 11 · 57 snapshots · spans 123 days

Genealogy 0 direct forks

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Metadata

Languages
ar de en es fr hi id it pt th tl vi
Quantizations
IQ3
Tags
transformers gguf facebook meta pytorch llama llama4 ar de en es fr

Related

Total size
44.2 GB
Files
3
Quantizations
2
Registered
2026-08-22 13:56
Last updated on HF
2026-06-11 06:18

Files by quantization

IQ3 1 file 44.2 GB
Llama-4-Scout-17B-16E-Instruct-abliterated-v2.i1-IQ3_M.gguf 44.2 GB 47e2e728 download
Auxiliary files 2 files 13.7 KB
README.md 8.86 KB c3223e8b download
.gitattributes 4.81 KB 17dc408d download

README current version from Hugging Face


base_model: jiangchengchengNLP/Llama-4-Scout-17B-16E-Instruct-abliterated-v2
language:

  • ar
  • de
  • en
  • es
  • fr
  • hi
  • id
  • it
  • pt
  • th
  • tl
  • vi
    library_name: transformers
    mradermacher:
    readme_rev: 1
    quantized_by: mradermacher
    tags:
  • facebook
  • meta
  • pytorch
  • llama
  • llama4

About

weighted/imatrix quants of https://huggingface.co/jiangchengchengNLP/Llama-4-Scout-17B-16E-Instruct-abliterated-v2

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

static quants are available at https://huggingface.co/mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-v2-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
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 22.3 for the desperate
GGUF i1-IQ1_M 24.7 mostly desperate
GGUF i1-IQ2_XXS 28.7
GGUF i1-IQ2_XS 31.9
GGUF i1-IQ2_S 32.4
GGUF i1-IQ2_M 35.6
GGUF i1-Q2_K_S 37.1 very low quality
GGUF i1-Q2_K 39.7 IQ3_XXS probably better
GGUF i1-IQ3_XXS 41.8 lower quality
GGUF i1-IQ3_XS 44.4
GGUF i1-Q3_K_S 46.8 IQ3_XS probably better
GGUF i1-IQ3_S 46.9 beats Q3_K*
GGUF i1-IQ3_M 47.6
PART 1 PART 2 i1-Q3_K_M 51.9 IQ3_S probably better
PART 1 PART 2 i1-Q3_K_L 56.1 IQ3_M probably better
PART 1 PART 2 i1-IQ4_XS 57.7
PART 1 PART 2 i1-Q4_0 61.3 fast, low quality
PART 1 PART 2 i1-Q4_K_S 61.6 optimal size/speed/quality
PART 1 PART 2 i1-Q4_K_M 65.5 fast, recommended
PART 1 PART 2 i1-Q4_1 67.7
PART 1 PART 2 i1-Q5_K_S 74.4
PART 1 PART 2 i1-Q5_K_M 76.6
PART 1 PART 2 i1-Q6_K 88.5 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. 2026-06-11Duplicate from mradermacher/Llama-4-Scout-17B-16E-Instruct-abliterated-v2-i1-...90f39a28.9 KB
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