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Cove-4eva/gemma-3-12b-it-norm-preserved-biprojected-abliterated

Cove-4eva Gemma 12B multimodal
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curl -H "Authorization: Bearer $ABL_KEY" \
     "https://abliteration.org/api/v1/models/Cove-4eva%2Fgemma-3-12b-it-norm-preserved-biprojected-abliterated"
Response includes
  • classification m1
  • files 18
  • benchmarks 16 entries
  • hub_downloads_all_time 7
  • author_summary 1 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
7
0
Likes
0
Model age
7mo ago
created 2026-03-21
Downloads over time
Now7→from7↑0%
77887 on Sep 177 on Nov 2SepOctNov
Sep 17 → Nov 2 · 47 snapshots · spans 46 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 3976 LM-Arena
LM Arena Elo 1335.3304642871612 LM-Arena
Arena-Elo-Lower 1326.1060034720686 LM-Arena
Arena-Elo-Upper 1344.5549251022537 LM-Arena
Arena-Rank 49 LM-Arena
Entertainment 1.3 UGI
Hazardous 2.9 UGI
Natural Intelligence 18.72 UGI
Political lean -11.7% UGI
Sensitive-Info 16.33 UGI
SocPol 1 UGI
UGI 20.89 UGI
Willingness (10) 3 UGI
W10-Adherence 0 UGI
W10-Direct 6 UGI
Writing 29.86 UGI

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
gemma
Tags
transformers safetensors gemma3 image-text-to-text conversational base_model:google/gemma-3-12b-it base_model:finetune:google/gemma-3-12b-it license:gemma text-generation-inference endpoints_compatible region:us

Related

Control vectors

Total size
22.7 GB
Files
18
Quantizations
1
Registered
2026-09-17 17:56
Last updated on HF
2026-03-21 08:55

Files by quantization

Auxiliary files 18 files 22.7 GB
model-00001-of-00005.safetensors 4.64 GB ******** download
model-00003-of-00005.safetensors 4.59 GB ******** download
model-00004-of-00005.safetensors 4.59 GB ******** download
model-00002-of-00005.safetensors 4.59 GB ******** download
model-00005-of-00005.safetensors 4.29 GB ******** download
tokenizer.json 31.8 MB ******** download
tokenizer.model 4.47 MB ******** download
tokenizer_config.json 1.15 MB 2cfe89ab download
model.safetensors.index.json 107 KB 25d054c8 download
config.json 2.94 KB 96e2ebbc download
README.md 1.56 KB 0c5549e1 download
chat_template.jinja 1.54 KB c5f13654 download
.gitattributes 1.53 KB 52373fe2 download
special_tokens_map.json 695 B 6728103d download
preprocessor_config.json 570 B b1e00fc1 download
generation_config.json 223 B 50cbbd78 download
processor_config.json 70.0 B 453c7966 download
added_tokens.json 38.0 B f9f1f4f5 download

README current version from Hugging Face


license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
base_model: google/gemma-3-12b-it
model_index:

  • name: gemma-3-12b-it-norm-preserved-biprojected-abliterated
    results:
    • task:
      type: text-generation
      name: Text Generation
      dataset:
      name: MMLU (5-shot)
      type: cais/mmlu
      config: main
      split: test
      args:
      num_few_shot: 5

      metrics:
      • type: acc
        value: 0.6985
        name: accuracy
        source:
        url: N/A
        name: N/A

gemma-3-12b-it-norm-preserved-biprojected-abliterated

This model was derived from google/gemma-3-12b-it.

Projected abliteration has been applied in determining refusal direction, along with a second round of removal of projected contribution onto the harmless direction of layer targeted for intervention.
Additionally, instead of subtracting/ablating away the refusal direction in toto, only the directional component of the refusal direction was removed, preserving the norms of the layers subjected to intervention.
The details of norm preservation can be found in the article on Norm-Preserving Biprojected Abliteration.
The net result should further reduce model damage compared to prior attempts; no subsequent fine-tuning was applied to repair damage.
This model refuses far less often than the original model, yet still retains awareness of safety and harms.

More details to follow.

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