← back to catalog · registered 2026-09-12 00:55

leomaurodesenv/electra-base-discriminator-trustairlab-jailbreak

Abliteration classifier · v1.0.0
?
Primary method

Unclassified

No clear signals of an abliteration technique in this model.
Confidence
UNKNOWN
Why this label 1 signal
No classification signals present. This may not be an abliterated model at all - it could be a repackaging, a merge with unrelated goals, or unrelated content that mentions the term.
  • no classification signals present (no abliterated, uncensored, or known producer/method markers)
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
189
Likes
0
Model age
4d ago
created 2026-09-12
Downloads over time
Now189from0↑0%
0691392080 on Sep 12189 on Sep 16Sep
Sep 12 → Sep 16 · 5 snapshots · spans 4 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
apache-2.0
Tags
transformers safetensors electra text-classification generated_from_trainer base_model:google/electra-base-discriminator base_model:finetune:google/electra-base-discriminator license:apache-2.0 endpoints_compatible region:us

Related

Total size
418 MB
Files
7
Quantizations
1
Registered
2026-09-12 00:55
Last updated on HF
2026-09-13 09:16

Files by quantization

Auxiliary files 7 files 418 MB
model.safetensors 418 MB 7028a6c5 download
training_args.bin 5.14 KB 72e6dd3d download
tokenizer.json 695 KB 05a08d51 download
README.md 1.86 KB 0906add8 download
.gitattributes 1.48 KB a6344aac download
config.json 1.01 KB f7796a57 download
tokenizer_config.json 322 B 76929107 download

README current version from Hugging Face


library_name: transformers
license: apache-2.0
base_model: google/electra-base-discriminator
tags:

  • generated_from_trainer
    metrics:
  • accuracy
    model-index:
  • name: electra-base-discriminator-trustairlab-jailbreak
    results: []

electra-base-discriminator-trustairlab-jailbreak

This model is a fine-tuned version of google/electra-base-discriminator on an unknown dataset.
It achieves the following results on the evaluation set:

  • Loss: 0.1905
  • Accuracy: 0.9388

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 50
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.2734 1.0 605 0.2173 0.9359
0.1792 2.0 1210 0.1904 0.9384
0.2315 3.0 1815 0.2045 0.9392
0.1664 4.0 2420 0.1956 0.9379
0.0775 5.0 3025 0.2416 0.9388

Framework versions

  • Transformers 5.2.0
  • Pytorch 2.10.0+cu128
  • Datasets 4.5.0
  • Tokenizers 0.22.2

README history 10 versions

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

  1. 2026-09-13End of trainingd984ec52 KB
    Loading...
  2. 2026-09-13Training in progress, epoch 1f173f922.1 KB
    Loading...
  3. 2026-09-12End of training8c143671.9 KB
    Loading...
  4. 2026-09-12Training in progress, epoch 1ebeac371.9 KB
    Loading...
  5. 2026-09-12End of traininga57d4462 KB
    Loading...
  6. 2026-09-12Training in progress, epoch 130ee5752 KB
    Loading...
  7. 2026-09-12End of training461eac52 KB
    Loading...
  8. 2026-09-12Training in progress, epoch 1480cd692 KB
    Loading...
  9. 2026-09-12End of trainingf17e5931.9 KB
    Loading...
  10. 2026-09-12Training in progress, epoch 17fa188f2 KB
    Loading...
Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every model page has an "Open in app" button that hands off directly to a local runtime of your choice - Infrahuman, LM Studio, or Ollama. No API keys, no subscription, no prompt leakage.