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mradermacher/T-lite-instruct-0.1-abliterated-GGUF

mradermacher GGUF second-order 8K ctx
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Response includes
  • classification m8
  • files 17
  • hub_downloads_all_time 11,316
  • 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 direct removal 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='IlyaGusev/T-lite-instruct-0.1-abliterated' (base has 'abliterated' marker, assume M1 default)
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
11K
1K last 30d - stable
Likes
4
Model age
2.2y ago
created 2024-07-21
Downloads over time
Now11.7K→from590↑1,880%
04.3K8.6K12.8K590 on Jul 24, 202411.7K on Oct 11Jul '24Nov '24Mar '25Jul '25Nov '25MarJul
Jul 24, 2024 → Oct 11 · 155 snapshots · spans 809 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.

Metadata

License
wtfpl
Languages
ru
Quantizations
F16 IQ3 IQ4 Q2_K Q3_K Q4_K Q5_K Q6_K Q8_0
Tags
transformers gguf ru base_model:IlyaGusev/T-lite-instruct-0.1-abliterated base_model:quantized:IlyaGusev/T-lite-instruct-0.1-abliterated license:wtfpl endpoints_compatible region:us conversational

Related

Total size
77.1 GB
Files
17
Quantizations
10
Registered
2026-08-22 13:56
Last updated on HF
2024-07-21 18:45

Files by quantization

F16 1 file 15.0 GB
T-lite-instruct-0.1-abliterated.f16.gguf 15.0 GB c6c4653b download
Q8_0 1 file 7.95 GB
T-lite-instruct-0.1-abliterated.Q8_0.gguf 7.95 GB 342d8c47 download
Q6_K 1 file 6.14 GB
T-lite-instruct-0.1-abliterated.Q6_K.gguf 6.14 GB 340c4da3 download
Q5_K 2 files 10.6 GB
T-lite-instruct-0.1-abliterated.Q5_K_M.gguf 5.34 GB 1c9abeee download
T-lite-instruct-0.1-abliterated.Q5_K_S.gguf 5.21 GB 166c6798 download
Q4_K 2 files 8.95 GB
T-lite-instruct-0.1-abliterated.Q4_K_M.gguf 4.58 GB f62de28f download
T-lite-instruct-0.1-abliterated.Q4_K_S.gguf 4.37 GB 9f5357fd download
IQ4 1 file 4.18 GB
T-lite-instruct-0.1-abliterated.IQ4_XS.gguf 4.18 GB a6165fbc download
Q3_K 3 files 11.2 GB
T-lite-instruct-0.1-abliterated.Q3_K_L.gguf 4.03 GB f7770480 download
T-lite-instruct-0.1-abliterated.Q3_K_M.gguf 3.74 GB 9e235685 download
T-lite-instruct-0.1-abliterated.Q3_K_S.gguf 3.41 GB 784b2a9e download
IQ3 3 files 10.2 GB
T-lite-instruct-0.1-abliterated.IQ3_M.gguf 3.52 GB 495d5768 download
T-lite-instruct-0.1-abliterated.IQ3_S.gguf 3.43 GB 048d970e download
T-lite-instruct-0.1-abliterated.IQ3_XS.gguf 3.28 GB 0904e1c6 download
Q2_K 1 file 2.96 GB
T-lite-instruct-0.1-abliterated.Q2_K.gguf 2.96 GB a623af40 download
Auxiliary files 2 files 6.75 KB
README.md 4.10 KB 4b0b05f9 download
.gitattributes 2.64 KB 6750f7ba download

README current version from Hugging Face


base_model: IlyaGusev/T-lite-instruct-0.1-abliterated
language:

  • ru
    library_name: transformers
    license: wtfpl
    quantized_by: mradermacher

About

static quants of https://huggingface.co/IlyaGusev/T-lite-instruct-0.1-abliterated

weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

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 Q2_K 3.3
GGUF IQ3_XS 3.6
GGUF Q3_K_S 3.8
GGUF IQ3_S 3.8 beats Q3_K*
GGUF IQ3_M 3.9
GGUF Q3_K_M 4.1 lower quality
GGUF Q3_K_L 4.4
GGUF IQ4_XS 4.6
GGUF Q4_K_S 4.8 fast, recommended
GGUF Q4_K_M 5.0 fast, recommended
GGUF Q5_K_S 5.7
GGUF Q5_K_M 5.8
GGUF Q6_K 6.7 very good quality
GGUF Q8_0 8.6 fast, best quality
GGUF f16 16.2 16 bpw, overkill

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.

README history 2 versions

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

  1. 2024-07-21auto-patch README.md76528364.1 KB
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  2. 2024-07-21uploaded from nethype/db203822d1231 B
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