lora_jailbreak_classifier_modernbert-base_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: ~571MB
LoRA Rank: 32
LoRA Alpha: 64
Target Modules: attn.Wqkv, attn.Wo, mlp.Wi, mlp.Wo

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: 32
Alpha: 64
Dropout: 0.1
Target Modules: attn.Wqkv, attn.Wo, mlp.Wi, mlp.Wo

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

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)?;