← back to catalog · registered 2026-09-26 20:57

mradermacher/Swift-Qwen3.8-27B-Uncensored-MTP-Terse-Coder-GGUF

mradermacher 27B GGUF second-order
curl -H "Authorization: Bearer $ABL_KEY" \
     "https://abliteration.org/api/v1/models/mradermacher%2FSwift-Qwen3.8-27B-Uncensored-MTP-Terse-Coder-GGUF"
Response includes
  • classification m5
  • files 8
  • author_summary 3275 models
  • readme_text full
10 credits · hourly refresh · ~4 KB payload Get an API key →
Abliteration classifier · v1.0.0
M5
Primary method

Mergekit merge

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.
  • merge tag / mergekit / dare-ties in tags or name
  • no unusual architecture pattern (regular merge)
  • abliterated marker present
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 · 30-day
0
Likes
1
Model age
today
created 2026-09-26

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
other
Languages
en
Quantizations
Q2_K Q4_K Q6_K Q8_0
Tags
transformers gguf merge lora abliterated uncensored reasoning coding token-efficient mtp qwen3_8 ai-red-team

Related

Total size
72.8 GB
Files
8
Quantizations
6
Registered
2026-09-26 20:57
Last updated on HF
2026-09-26 21:51

Files by quantization

Q8_0 2 files 27.6 GB
Swift-Qwen3.8-27B-Uncensored-MTP-Terse-Coder.Q8_0.gguf 27.1 GB fc9ca80b download
Swift-Qwen3.8-27B-Uncensored-MTP-Terse-Coder.mmproj-Q8_0.gguf 600 MB d7ba7eb4 download
Q6_K 1 file 20.9 GB
Swift-Qwen3.8-27B-Uncensored-MTP-Terse-Coder.Q6_K.gguf 20.9 GB c4437fba download
Q4_K 1 file 14.7 GB
Swift-Qwen3.8-27B-Uncensored-MTP-Terse-Coder.Q4_K_S.gguf 14.7 GB 8962cc41 download
Q2_K 1 file 10.1 GB
Swift-Qwen3.8-27B-Uncensored-MTP-Terse-Coder.Q2_K.gguf 10.1 GB a52ce987 download
F16 1 file 885 MB
Swift-Qwen3.8-27B-Uncensored-MTP-Terse-Coder.mmproj-f16.gguf 885 MB 1e02eaad download
Auxiliary files 2 files 5.00 KB
README.md 2.96 KB 4c71abcb download
.gitattributes 2.03 KB fbbf7ddb download

README current version from Hugging Face


base_model: vwdubb/Swift-Qwen3.8-27B-Uncensored-MTP-Terse-Coder
language:

  • en
    library_name: transformers
    license: other
    mradermacher:
    readme_rev: 1
    quantized_by: mradermacher
    tags:
  • merge
  • lora
  • abliterated
  • uncensored
  • reasoning
  • coding
  • token-efficient
  • mtp
  • qwen3_8
  • ai-red-team
  • conversational

About

static quants of https://huggingface.co/vwdubb/Swift-Qwen3.8-27B-Uncensored-MTP-Terse-Coder

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

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 mmproj-Q8_0 0.7 multi-modal supplement
GGUF mmproj-f16 1.0 multi-modal supplement
GGUF Q2_K 11.0
GGUF Q4_K_S 15.9 fast, recommended

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.

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