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marx161-cmd/Llama-3.2-1B-Double-Abliterated-d2p0-r1p0-RowRestore

marx161-cmd Llama 1.2B
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Response includes
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  • files 11
  • benchmarks 21 entries
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  • author_summary 5 models
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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
33
9 last 30d - stable
Likes
0
Descendants
1
in 1 direct fork
Model age
4mo ago
created 2026-06-05
Downloads over time
Now39→from13↑200%
1222324213 on Jun 1039 on Oct 1139 on Oct 7JunJulAugSepOct
Jun 10 → Oct 11 · 57 snapshots · spans 123 days

Benchmarks

Portrait before abliteration
Benchmarks of the base model as it stood before the refusal-removal operation. Compare with the numbers above to see what the operation cost.
Benchmark Score Source
Arena-Battles 8523 LM-Arena
LM Arena Elo 1067.4348646816593 LM-Arena
Arena-Elo-Lower 1060.145342091014 LM-Arena
Arena-Elo-Upper 1074.7243872723045 LM-Arena
Arena-Rank 207 LM-Arena
BBH average 0.3342846024127643 OpenLLM-v2
IFEval instruct 0.6294964028776978 OpenLLM-v2
IFEval-Prompt 0.5101663585951941 OpenLLM-v2
MATH lvl 5 0.02945619335347432 OpenLLM-v2
MMLU-Pro 0.16821808510638298 OpenLLM-v2
Entertainment 0.2 UGI
Hazardous 0 UGI
Natural Intelligence 6.89 UGI
Political lean -6.0% UGI
Sensitive-Info 2.34 UGI
SocPol 0.5 UGI
UGI 6.56 UGI
Willingness (10) 1.5 UGI
W10-Adherence 1 UGI
W10-Direct 2 UGI
Writing 11.82 UGI

Genealogy 1 direct fork

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Metadata

License
llama3.2
Tags
transformers safetensors llama text-generation llama-3.2 disinhibition model-editing row-restoration research conversational base_model:meta-llama/Llama-3.2-1B-Instruct base_model:finetune:meta-llama/Llama-3.2-1B-Instruct

Related

Total size
2.30 GB
Files
11
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-06-05 16:30

Files by quantization

Auxiliary files 11 files 2.32 GB
model.safetensors 2.30 GB ac8fd60b download
tokenizer.json 16.4 MB 6b9e4e7f download
LICENSE 7.49 KB 83f13c3a download
chat_template.jinja 3.74 KB 1bad6a0f download
README.md 3.04 KB 68fadef1 download
.gitattributes 1.53 KB 52373fe2 download
MODEL_CARD_DRAFT.md 1.51 KB 7ca41282 download
config.json 894 B 13536d74 download
tokenizer_config.json 325 B aadc141c download
NOTICE 206 B 7789c383 download
generation_config.json 183 B 920e1e14 download

README current version from Hugging Face


license: llama3.2
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:

  • llama
  • llama-3.2
  • text-generation
  • disinhibition
  • model-editing
  • row-restoration
  • research
    library_name: transformers
    pipeline_tag: text-generation

Llama 3.2 1B Instruct d2p0-r1p0 Row-Restored Double Edit

Built with Llama.

This is a derivative of meta-llama/Llama-3.2-1B-Instruct using a sequential double edit:

  1. Disinhibition direction at scale 2.0
  2. Row-norm restoration
  3. Refusal direction at scale 1.0
  4. Row-norm restoration

The goal was to preserve most of the disinhibition-only hedge reduction while also reducing refusal-marker responses in the sampled refusal harness.

Edit

  • Base model: meta-llama/Llama-3.2-1B-Instruct
  • Direction A: disinhibition_purified.pt
  • Direction A global scale: 2.0
  • Direction B: refusal_purified.pt
  • Direction B global scale: 1.0
  • Applied layers: 1-15
  • Stack method: sequential row-norm restoration after each direction pass

Results

Marker-eval results:

Bucket Result
Harmful refusal markers 0/80
Harmful safety markers 1/80
Harmless refusal markers 1/80
Harmless coherence flags 1/80
Opinions hedge 24/120
Opinions neutrality 21/120
Explicit-neutral hedge 9/25
Explicit-neutral neutrality 16/25
Factual hedge 3/42
Factual neutrality 0/42
Coherence hedge 1/28
Edge-case hedge 3/33
Treadon coherence flags 0

For comparison:

  • Base opinion hedge: 95/120
  • Disinhibition-only s2.0 opinion hedge: 19/120
  • This double edit opinion hedge: 24/120

The double edit kept most of the disinhibition-only improvement while reducing sampled harmful refusal markers to 0/80.

Method Notes

This checkpoint was selected from regular, row-restored, and orthogonalized stack candidates.

The key control result: orthogonalizing the refusal vector against the disinhibition vector produced near-zero residual cosine, but did not beat the plain row-restored stack on behavior.

Best stack comparison:

Candidate Opinions hedge Explicit-neutral hedge Harmful refusal Harmless refusal
regular-r1p0 30/120 8/25 0/40 1/40
rowrestore-r1p0 24/120 9/25 0/40 1/40
orthrow-r1p0 26/120 9/25 0/40 1/40

This suggests the main stacking penalty in this run was better addressed by preserving row geometry between edits than by removing direct vector overlap.

Limitations

These are marker-based evals, not full semantic evaluations. They should not be read as proof of safety, factuality, or universal helpfulness.

The one harmless refusal-marker hit and one harmless short-output flag should be manually inspected before making stronger claims.

Use must comply with the Llama 3.2 Community License and Meta's Acceptable Use Policy.

License

This model is distributed under the Llama 3.2 Community License. See LICENSE and NOTICE.

README history 1 version

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

  1. 2026-06-05Upload Llama 3.2 1B row-restored double-edit checkpoint1bbcf023 KB
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