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HFCK99/Qwen3.8-27B-Uncensored-Cyber

HFCK99 Qwen 28B multimodal second-order
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Response includes
  • classification m1
  • files 19
  • hub_downloads_all_time 125
  • author_summary 6 models
  • readme_text full
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Abliteration classifier · v1.0.0
M1
Primary method

Direct removal

No other method signals detected in this model.
Confidence
MEDIUM
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=0 (base model)
  • no specific method indicators - defaulting to M1 (most common)
Refusal direction extracted via
Extraction technique

Difference-of-means

Confidence
MEDIUM
Why we say so
primary_method=M1; difference-of-means is the reference extraction for M1/M3 (Arditi 2024)
Downloads · lifetime
125
72 last 30d - active
Likes
2
Descendants
2
in 2 direct forks
Model age
7w ago
created 2026-08-20
Downloads over time
Now168→from45↑273%
398613318045 on Aug 19168 on Oct 11AugSepOct
Aug 19 → Oct 11 · 48 snapshots · spans 53 days

Genealogy 2 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
apache-2.0
Tags
safetensors qwen3_5 uncensored abliterated qwen3 cyber image-text-to-text conversational base_model:philbert440/Qwen3.8-27B-Uncensored-Aggressive base_model:finetune:philbert440/Qwen3.8-27B-Uncensored-Aggressive license:apache-2.0 region:us

Related

Total size
51.7 GB
Files
19
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-08-20 14:33

Files by quantization

Auxiliary files 19 files 51.8 GB
model-00001-of-00002.safetensors 46.4 GB 2a941d6f download
model-00002-of-00002.safetensors 4.55 GB a7947a60 download
model-mtp.safetensors 810 MB 90fa0e3e download
tokenizer.json 19.1 MB 06b95093 download
vocab.json 6.41 MB 0aa0ce06 download
merges.txt 3.20 MB a494e019 download
model.safetensors.index.json 105 KB 1a53e75c download
LICENSE 11.3 KB f938136e download
chat_template.jinja 8.74 KB c0c686f9 download
config.json 3.60 KB 36324968 download
README.md 1.94 KB bfb7afa5 download
.gitattributes 1.53 KB 52373fe2 download
processor_config.json 1.16 KB 33818c7f download
tokenizer_config.json 1.10 KB d1a20cc3 download
preprocessor_config.json 390 B 2ea84a43 download
video_preprocessor_config.json 385 B 3ba673a5 download
abliteration_meta.json 294 B b02d96e4 download
crc32.txt 238 B 6de5ee6a download
generation_config.json 214 B 8b9f95da download

README current version from Hugging Face


license: apache-2.0
base_model: philbert440/Qwen3.8-27B-Uncensored-Aggressive
tags:

  • uncensored
  • abliterated
  • qwen3
  • cyber
  • image-text-to-text
    pipeline_tag: image-text-to-text

Qwen3.8-27B-Uncensored-Cyber

Cyber-specialized de-refusal of Qwen3.8-27B: fully open on the cyber/offensive-security domain while
keeping reasoning, factual accuracy, and coherence intact. Vision tower and MTP speculative-decoding head
are preserved (full multimodal, image-text-to-text).

Recipe (v2)

Built on the α=1.15 Aggressive base (recipe-v2 single-direction refusal ablation), then a residual-cyber
peel
: a cyber-pointed refusal direction (from the cyber-offensive training set vs broad harmless) removed by
clean norm-preserving projection (β=1.0) applied only to the deeper layers (retain the first 4 layers,
apply_from=4). Retaining the early feature-extraction layers is what preserves general capability — the
lesson from the community's Qwen3 abliterations — so we reach 100% cyber-openness without wrecking the model.

Evaluation (bf16, larger-sample, Claude-judged; cyber = 100 held-out cyber-offensive prompts, regex refusal harness)

cyber-open ↑ confab ↓ factual ↑ gsm8k ↑ degen ↓
Cyber (this model, v2) 100/100 0.867 1.00 0.80 0.00
previous Cyber build 93/100 1.00 0.933 0.825 0.00

The v2 recipe is more cyber-open (100 vs 93), less confabulating (0.87 vs 1.0), and more factually
accurate (1.0 vs 0.93)
than the previous Cyber, with equivalent reasoning and zero degeneration.

Quants

  • -W4A16-AWQ — 4-bit weight AWQ (compressed-tensors), MTP head grafted
  • -NVFP4 — NVFP4 (E2M1 4-bit / FP8 scales), MTP head grafted
  • -GGUF — llama.cpp GGUF quants + vision mmproj + MTP head

Note

Uncensored / de-refused, tuned to fully answer cyber and offensive-security questions. Use responsibly and
in compliance with applicable law.

README history 1 version

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

  1. 2026-08-20Duplicate from philbert440/Qwen3.8-27B-Uncensored-Cyberb5b0e391.9 KB
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