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vllm-sr/lora_jailbreak_classifier_bert-base-uncased_model

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Abliteration classifier · v1.0.0
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Downloads · 30-day
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Model age
12mo ago
created 2025-09-13

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Metadata

License
apache-2.0
Languages
en
Tags
candle safetensors bert lora semantic-router classification-classification text-classification rust en base_model:google-bert/bert-base-uncased base_model:adapter:google-bert/bert-base-uncased license:apache-2.0

Related

Total size
418 MB
Files
11
Quantizations
1
Registered
2026-10-02 09:58
Last updated on HF
2025-12-10 23:19

Files by quantization

Auxiliary files 11 files 419 MB
model.safetensors 418 MB 53d99da8 download
tokenizer.json 695 KB 688882a7 download
vocab.txt 226 KB fb140275 download
README.md 2.30 KB e577a85e download
.gitattributes 1.48 KB a6344aac download
tokenizer_config.json 1.19 KB dd521218 download
config.json 749 B 33d128dd download
lora_config.json 195 B e063cf38 download
jailbreak_type_mapping.json 128 B 1d760ed3 download
special_tokens_map.json 125 B a8b3208c download
label_mapping.json 96.0 B 0f0337fe download

README current version from Hugging Face


license: apache-2.0
base_model: bert-base-uncased
tags:

  • lora
  • semantic-router
  • classification-classification
  • text-classification
  • candle
  • rust
    language:
  • en
    pipeline_tag: text-classification
    library_name: candle

lora_jailbreak_classifier_bert-base-uncased_model

Model Description

This is a LoRA (Low-Rank Adaptation) fine-tuned model based on bert-base-uncased for Multi-task classification.

This model is part of the semantic-router project and is optimized for use with the Candle framework in Rust.

Model Details

  • Base Model: bert-base-uncased
  • Task: Classification Classification
  • Framework: Candle (Rust)
  • Model Size: ~418MB
  • LoRA Rank: 16
  • LoRA Alpha: 16
  • Target Modules: attention.self.query, attention.self.value, attention.output.dense, intermediate.dense, output.dense

Usage

With semantic-router (Recommended)

from semantic_router import SemanticRouter

# The model will be automatically downloaded and used
router = SemanticRouter()
results = router.classify_batch(["Your text here"])

With Candle (Rust)

use candle_core::{Device, Tensor};
use candle_transformers::models::bert::BertModel;

// Load the model using Candle
let device = Device::Cpu;
let model = BertModel::load(&device, &config, &weights)?;

Training Details

This model was fine-tuned using LoRA (Low-Rank Adaptation) technique:

  • Rank: 16
  • Alpha: 16
  • Dropout: 0.1
  • Target Modules: attention.self.query, attention.self.value, attention.output.dense, intermediate.dense, output.dense

Performance

Multi-task classification

For detailed performance metrics, see the training results.

Files

  • model.safetensors: LoRA adapter weights
  • config.json: Model configuration
  • lora_config.json: LoRA-specific configuration
  • tokenizer.json: Tokenizer configuration
  • label_mapping.json: Label mappings for classification

Citation

If you use this model, please cite:

@misc{semantic-router-lora,
  title={LoRA Fine-tuned Models for Semantic Router},
  author={Semantic Router Team},
  year={2025},
  url={https://github.com/vllm-project/semantic-router}
}

License

Apache 2.0

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