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Upload lora_jailbreak_classifier_roberta-base_model LoRA model

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: roberta-base
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+ tags:
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+ - lora
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+ - semantic-router
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+ - classification-classification
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+ - text-classification
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+ - candle
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+ - rust
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+ language:
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+ - en
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+ pipeline_tag: text-classification
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+ library_name: candle
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+ ---
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+
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+ # lora_jailbreak_classifier_roberta-base_model
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+
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+ ## Model Description
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+
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+ This is a LoRA (Low-Rank Adaptation) fine-tuned model based on **roberta-base** for Multi-task classification.
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+
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+ This model is part of the [semantic-router](https://github.com/vllm-project/semantic-router) project and is optimized for use with the Candle framework in Rust.
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+
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+ ## Model Details
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+
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+ - **Base Model**: roberta-base
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+ - **Task**: Classification Classification
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+ - **Framework**: Candle (Rust)
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+ - **Model Size**: ~476MB
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+ - **LoRA Rank**: 16
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+ - **LoRA Alpha**: 16
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+ - **Target Modules**: attention.self.query, attention.self.value, attention.output.dense, intermediate.dense, output.dense
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+
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+ ## Usage
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+
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+ ### With semantic-router (Recommended)
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+
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+ ```python
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+ from semantic_router import SemanticRouter
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+
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+ # The model will be automatically downloaded and used
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+ router = SemanticRouter()
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+ results = router.classify_batch(["Your text here"])
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+ ```
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+
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+ ### With Candle (Rust)
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+
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+ ```rust
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+ use candle_core::{Device, Tensor};
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+ use candle_transformers::models::bert::BertModel;
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+
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+ // Load the model using Candle
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+ let device = Device::Cpu;
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+ let model = BertModel::load(&device, &config, &weights)?;
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+ ```
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+
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+ ## Training Details
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+
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+ This model was fine-tuned using LoRA (Low-Rank Adaptation) technique:
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+
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+ - **Rank**: 16
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+ - **Alpha**: 16
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+ - **Dropout**: 0.1
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+ - **Target Modules**: attention.self.query, attention.self.value, attention.output.dense, intermediate.dense, output.dense
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+
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+ ## Performance
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+
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+ Multi-task classification
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+
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+ For detailed performance metrics, see the [training results](https://github.com/vllm-project/semantic-router/blob/main/training-result.md).
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+
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+ ## Files
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+
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+ - `model.safetensors`: LoRA adapter weights
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+ - `config.json`: Model configuration
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+ - `lora_config.json`: LoRA-specific configuration
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+ - `tokenizer.json`: Tokenizer configuration
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+ - `label_mapping.json`: Label mappings for classification
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+
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+ ## Citation
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+
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+ If you use this model, please cite:
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+
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+ ```bibtex
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+ @misc{semantic-router-lora,
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+ title={LoRA Fine-tuned Models for Semantic Router},
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+ author={Semantic Router Team},
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+ year={2025},
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+ url={https://github.com/vllm-project/semantic-router}
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+ }
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+ ```
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+
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+ ## License
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+
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+ Apache 2.0
config.json ADDED
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+ {
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+ "architectures": [
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+ "RobertaForSequenceClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "bos_token_id": 0,
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+ "classifier_dropout": null,
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+ "hidden_act": "gelu",
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "layer_norm_eps": 1e-05,
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+ "max_position_embeddings": 514,
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+ "model_type": "roberta",
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+ "num_attention_heads": 12,
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+ "position_embedding_type": "absolute",
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+ "transformers_version": "4.56.1",
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+ "use_cache": true,
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+ "vocab_size": 50265,
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+ "id2label": {
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+ "0": "benign",
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+ "1": "jailbreak"
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+ },
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+ "label2id": {
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+ "benign": 0,
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+ "jailbreak": 1
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+ }
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+ }
label_mapping.json ADDED
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+ {"label_to_id": {"benign": 0, "jailbreak": 1}, "id_to_label": {"0": "benign", "1": "jailbreak"}}
lora_config.json ADDED
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+ {"rank": 16, "alpha": 16, "dropout": 0.1, "target_modules": ["attention.self.query", "attention.self.value", "attention.output.dense", "intermediate.dense", "output.dense"]}
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