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leomaurodesenv/nli-MiniLM2-L6-H768-jailbreakv-28k

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  • files 7
  • hub_downloads_all_time 291
  • author_summary 30 models
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Abliteration classifier · v1.0.0
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Downloads · lifetime
291
21 last 30d - cooling
Likes
0
Model age
6w ago
created 2026-09-14
Downloads over time
Now316→from0↑0%
01162323480 on Sep 14316 on Oct 31SepOct
Sep 14 → Oct 31 · 48 snapshots · spans 47 days

Genealogy 0 direct forks

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Metadata

License
apache-2.0
Tags
transformers safetensors roberta text-classification generated_from_trainer base_model:cross-encoder/nli-MiniLM2-L6-H768 base_model:finetune:cross-encoder/nli-MiniLM2-L6-H768 license:apache-2.0 text-embeddings-inference endpoints_compatible region:us

Related

Total size
313 MB
Files
7
Quantizations
1
Registered
2026-09-14 01:56
Last updated on HF
2026-09-16 02:41

Files by quantization

Auxiliary files 7 files 317 MB
model.safetensors 313 MB d19cbbf8 download
training_args.bin 5.14 KB bcc22013 download
tokenizer.json 3.39 MB 72a0cbc8 download
README.md 2.14 KB 919ba337 download
.gitattributes 1.48 KB a6344aac download
config.json 922 B f36112ed download
tokenizer_config.json 359 B a6d24b8e download

README current version from Hugging Face


library_name: transformers
license: apache-2.0
base_model: cross-encoder/nli-MiniLM2-L6-H768
tags:

  • generated_from_trainer
    metrics:
  • accuracy
    model-index:
  • name: nli-MiniLM2-L6-H768-jailbreakv-28k
    results: []

nli-MiniLM2-L6-H768-jailbreakv-28k

This model is a fine-tuned version of cross-encoder/nli-MiniLM2-L6-H768 on an unknown dataset.
It achieves the following results on the evaluation set:

  • Loss: 0.0000
  • Accuracy: 1.0

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
0.0002 1.0 1121 0.0001 1.0
0.0001 2.0 2242 0.0000 1.0
0.0000 3.0 3363 0.0000 1.0
0.0000 4.0 4484 0.0000 1.0
0.0000 5.0 5605 0.0000 1.0
0.0000 6.0 6726 0.0000 1.0
0.0000 7.0 7847 0.0000 1.0
0.0000 8.0 8968 0.0000 1.0
0.0000 9.0 10089 0.0000 1.0
0.0000 10.0 11210 0.0000 1.0

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. 2026-09-16End of trainingfa846752.1 KB
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  2. 2026-09-16Training in progress, epoch 11018c762 KB
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  3. 2026-09-15End of training0c942d92.1 KB
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  4. 2026-09-15Training in progress, epoch 1a5ac2812.1 KB
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  5. 2026-09-15End of trainingba3e7d22.1 KB
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  6. 2026-09-15Training in progress, epoch 165c60731.9 KB
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  7. 2026-09-14End of trainingba0ca812.1 KB
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  8. 2026-09-14Training in progress, epoch 17ba22b72.1 KB
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  9. 2026-09-14End of traininga6d8a202.1 KB
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  10. 2026-09-14Training in progress, epoch 1347b0042.2 KB
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