← back to catalog · registered 2026-08-22 13:56

leomaurodesenv/distilbert-base-uncased-disaster-tweet-jailbreaking

Your rig guess connected
? Why do I need an app?
Reading your rig…

This is a rough estimate. Install the free app - we'll show exact numbers.

Reading real hardware from your app right now. Numbers below are exact.

Below is the per-quantization compatibility for this model.

curl -H "Authorization: Bearer $ABL_KEY" \
     "https://abliteration.org/api/v1/models/leomaurodesenv%2Fdistilbert-base-uncased-disaster-tweet-jailbreaking"
Response includes
  • classification unknown
  • files 7
  • hub_downloads_all_time 113
  • author_summary 30 models
  • readme_text full
10 credits · hourly refresh · ~4 KB payload Get an API key →
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
113
8 last 30d - cooling
Likes
0
Model age
7w ago
created 2026-08-21
Downloads over time
Now114→from88↑30%
879710711788 on Aug 19114 on Oct 12114 on Oct 4AugSepOct
Aug 19 → Oct 12 · 49 snapshots · spans 54 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 distilbert text-classification generated_from_trainer base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:apache-2.0 text-embeddings-inference endpoints_compatible region:us

Related

Total size
255 MB
Files
7
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-08-21 20:38

Files by quantization

Auxiliary files 7 files 256 MB
model.safetensors 255 MB 9cde2d11 download
training_args.bin 5.14 KB 053c2554 download
tokenizer.json 695 KB 05a08d51 download
README.md 1.81 KB 44f37875 download
.gitattributes 1.48 KB a6344aac download
config.json 771 B b21b7dee download
tokenizer_config.json 322 B 76929107 download

README current version from Hugging Face


library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:

  • generated_from_trainer
    metrics:
  • accuracy
    model-index:
  • name: distilbert-base-uncased-disaster-tweet-jailbreaking
    results: []

distilbert-base-uncased-disaster-tweet-jailbreaking

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

  • Loss: 0.6027
  • Accuracy: 0.7113

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
  • 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
0.5332 1.0 243 0.6080 0.6680
0.5117 2.0 486 0.6029 0.7134
0.3544 3.0 729 0.7232 0.7031
0.2960 4.0 972 0.6870 0.6928
0.1987 5.0 1215 0.9753 0.7093

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. 2026-08-21End of trainingb0164d91.8 KB
    Loading...
  2. 2026-08-21Training in progress, epoch 1e4f578c2.1 KB
    Loading...
  3. 2026-08-21End of training80b81931.7 KB
    Loading...
  4. 2026-08-21Training in progress, epoch 181e8f812 KB
    Loading...
  5. 2026-08-21End of traininge52c21d1.7 KB
    Loading...
  6. 2026-08-21Training in progress, epoch 180311182.1 KB
    Loading...
  7. 2026-08-21End of trainingffe13aa1.7 KB
    Loading...
  8. 2026-08-21Training in progress, epoch 182850ea2.1 KB
    Loading...
  9. 2026-08-21End of training950cdc51.7 KB
    Loading...
  10. 2026-08-21Training in progress, epoch 11c14eea2 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 Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

Open in Abliteration