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mradermacher/FluentlyLM-Prinum-abliterated-i1-GGUF

mradermacher Qwen GGUF second-order 33K ctx
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Response includes
  • classification m8
  • files 25
  • hub_downloads_all_time 15,416
  • author_summary 3324 models
  • readme_text full
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Abliteration classifier · v1.0.0
M8
Primary method

Repackaging (quantization)

Applied on top of layer-wise ablation inherited from the base model.
Confidence
HIGH
Inherited from base model
Why this label 3 signals
Producer identity confirmed by naming conventions, tags or the model card. This label is very unlikely to change.
  • author=mradermacher (M8 quantization producer, never originator)
  • is_gguf=1
  • base_model='huihui-ai/FluentlyLM-Prinum-abliterated' (base is huihui-ai model (M3))
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
15K
1K last 30d - cooling
Likes
3
Model age
19mo ago
created 2025-03-03

Training datasets

Corpora the author lists in the model card. Datasets tracked in our /datasets catalog carry a category badge linking to the workflow stage. Others open on Hugging Face.

Downloads over time
Now15.9K→from93↑17,008%
05.8K11.7K17.5K93 on Feb 26, 202515.9K on Oct 11Feb '25May '25Aug '25Nov '25FebMayAug
Feb 26, 2025 → Oct 11 · 125 snapshots · spans 592 days

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.

Variants by this author 2 formats · 2K downloads combined

The same weights this author released in different packaging. Pick the format that matches your runtime.

Metadata

License
mit
Languages
en fr es ru zh ja fa code
Quantizations
IQ1 IQ2 IQ3 IQ4 Q2_K Q3_K Q4 Q4_K Q5_K Q6_K
Tags
transformers gguf abliterated uncensored fluently-lm fluently prinum instruct trained math roleplay reasoning

Related

Total size
327 GB
Files
25
Quantizations
11
Registered
2026-08-22 13:56
Last updated on HF
2025-03-03 11:19

Files by quantization

Q6_K 1 file 25.0 GB
FluentlyLM-Prinum-abliterated.i1-Q6_K.gguf 25.0 GB 37beb8f0 download
Q5_K 2 files 42.7 GB
FluentlyLM-Prinum-abliterated.i1-Q5_K_M.gguf 21.7 GB e68b446c download
FluentlyLM-Prinum-abliterated.i1-Q5_K_S.gguf 21.1 GB 1dd1ba76 download
Q4 2 files 36.6 GB
FluentlyLM-Prinum-abliterated.i1-Q4_1.gguf 19.2 GB c79d0e8b download
FluentlyLM-Prinum-abliterated.i1-Q4_0.gguf 17.4 GB ccf28c44 download
Q4_K 2 files 36.0 GB
FluentlyLM-Prinum-abliterated.i1-Q4_K_M.gguf 18.5 GB e0a59ab3 download
FluentlyLM-Prinum-abliterated.i1-Q4_K_S.gguf 17.5 GB 70c8fc15 download
IQ4 1 file 16.5 GB
FluentlyLM-Prinum-abliterated.i1-IQ4_XS.gguf 16.5 GB 4eab8730 download
Q3_K 3 files 44.3 GB
FluentlyLM-Prinum-abliterated.i1-Q3_K_L.gguf 16.1 GB e82852ed download
FluentlyLM-Prinum-abliterated.i1-Q3_K_M.gguf 14.8 GB 63a59335 download
FluentlyLM-Prinum-abliterated.i1-Q3_K_S.gguf 13.4 GB 2b4b18f3 download
IQ3 4 files 52.0 GB
FluentlyLM-Prinum-abliterated.i1-IQ3_M.gguf 13.8 GB 60e8b51c download
FluentlyLM-Prinum-abliterated.i1-IQ3_S.gguf 13.4 GB 71802e31 download
FluentlyLM-Prinum-abliterated.i1-IQ3_XS.gguf 12.8 GB aa9b2b77 download
FluentlyLM-Prinum-abliterated.i1-IQ3_XXS.gguf 12.0 GB 54d0f3a2 download
Q2_K 2 files 22.2 GB
FluentlyLM-Prinum-abliterated.i1-Q2_K.gguf 11.5 GB 943cf59e download
FluentlyLM-Prinum-abliterated.i1-Q2_K_S.gguf 10.7 GB ba6ef761 download
IQ2 4 files 37.8 GB
FluentlyLM-Prinum-abliterated.i1-IQ2_M.gguf 10.5 GB cdd8728f download
FluentlyLM-Prinum-abliterated.i1-IQ2_S.gguf 9.67 GB b7b517c6 download
FluentlyLM-Prinum-abliterated.i1-IQ2_XS.gguf 9.27 GB 82c4dc10 download
FluentlyLM-Prinum-abliterated.i1-IQ2_XXS.gguf 8.41 GB 21520b92 download
IQ1 2 files 14.2 GB
FluentlyLM-Prinum-abliterated.i1-IQ1_M.gguf 7.39 GB af7e9799 download
FluentlyLM-Prinum-abliterated.i1-IQ1_S.gguf 6.77 GB 50335c73 download
Auxiliary files 2 files 9.33 KB
README.md 6.03 KB fb6f0317 download
.gitattributes 3.29 KB ee1f9169 download

README current version from Hugging Face


base_model: huihui-ai/FluentlyLM-Prinum-abliterated
datasets:

  • fluently-sets/ultraset
  • fluently-sets/ultrathink
  • fluently-sets/reasoning-1-1k
  • fluently-sets/MATH-500-Overall
    language:
  • en
  • fr
  • es
  • ru
  • zh
  • ja
  • fa
  • code
    library_name: transformers
    license: mit
    quantized_by: mradermacher
    tags:
  • abliterated
  • uncensored
  • fluently-lm
  • fluently
  • prinum
  • instruct
  • trained
  • math
  • roleplay
  • reasoning
  • axolotl
  • unsloth
  • argilla
  • qwen2

About

weighted/imatrix quants of https://huggingface.co/huihui-ai/FluentlyLM-Prinum-abliterated

static quants are available at https://huggingface.co/mradermacher/FluentlyLM-Prinum-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 7.4 for the desperate
GGUF i1-IQ1_M 8.0 mostly desperate
GGUF i1-IQ2_XXS 9.1
GGUF i1-IQ2_XS 10.1
GGUF i1-IQ2_S 10.5
GGUF i1-IQ2_M 11.4
GGUF i1-Q2_K_S 11.6 very low quality
GGUF i1-Q2_K 12.4 IQ3_XXS probably better
GGUF i1-IQ3_XXS 12.9 lower quality
GGUF i1-IQ3_XS 13.8
GGUF i1-Q3_K_S 14.5 IQ3_XS probably better
GGUF i1-IQ3_S 14.5 beats Q3_K*
GGUF i1-IQ3_M 14.9
GGUF i1-Q3_K_M 16.0 IQ3_S probably better
GGUF i1-Q3_K_L 17.3 IQ3_M probably better
GGUF i1-IQ4_XS 17.8
GGUF i1-Q4_0 18.8 fast, low quality
GGUF i1-Q4_K_S 18.9 optimal size/speed/quality
GGUF i1-Q4_K_M 20.0 fast, recommended
GGUF i1-Q4_1 20.7
GGUF i1-Q5_K_S 22.7
GGUF i1-Q5_K_M 23.4
GGUF i1-Q6_K 27.0 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 2 versions

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

  1. 2025-03-03auto-patch README.md62986186 KB
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  2. 2025-03-03uploaded from marco5f47f60247 B
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