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README.md

@@ -1,206 +1,52 @@
 ---
+license: apache-2.0
 base_model: llm-semantic-router/mmbert-32k-yarn
-library_name: peft
 tags:
-- base_model:adapter:llm-semantic-router/mmbert-32k-yarn
+- jailbreak-detection
+- security
+- text-classification
 - lora
-- transformers
+- peft
+datasets:
+- lmsys/toxic-chat
+- OpenSafetyLab/Salad-Data
+language:
+- en
+metrics:
+- accuracy
+- f1
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# mmBERT-32K Jailbreak Detector (LoRA)
 
+LoRA adapter for jailbreak/prompt injection detection based on mmBERT-32K-YaRN.
 
 ## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-[More Information Needed]
-
-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-[More Information Needed]
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-
-### Results
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-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
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-[More Information Needed]
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-#### Software
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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-[More Information Needed]
-### Framework versions
-
-- PEFT 0.18.1
+- **Base Model**: llm-semantic-router/mmbert-32k-yarn
+- **LoRA Rank**: 48
+- **LoRA Alpha**: 96
+- **Training**: 8 epochs with heavy short-pattern augmentation
+
+## Performance
+- **Validation Accuracy**: 98.16%
+- **F1 Score**: 98.15%
+- **Precision**: 98.36%
+- **Recall**: 97.95%
+
+## Key Improvements
+This model includes heavy oversampling of short jailbreak patterns to improve generalization:
+- Detects short patterns like "DAN", "jailbreak", "Developer mode" with 100% confidence
+- Properly handles both short and long jailbreak attempts
+
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from peft import PeftModel
+
+base_model = "llm-semantic-router/mmbert-32k-yarn"
+lora_path = "llm-semantic-router/mmbert32k-jailbreak-detector-lora"
+
+tokenizer = AutoTokenizer.from_pretrained(lora_path)
+base = AutoModelForSequenceClassification.from_pretrained(base_model, num_labels=2)
+model = PeftModel.from_pretrained(base, lora_path)
+```

adapter_config.json

@@ -16,7 +16,7 @@
   "layers_pattern": null,
   "layers_to_transform": null,
   "loftq_config": {},
-  "lora_alpha": 64,
+  "lora_alpha": 96,
   "lora_bias": false,
   "lora_dropout": 0.1,
   "megatron_config": null,
@@ -29,14 +29,14 @@
   "peft_type": "LORA",
   "peft_version": "0.18.1",
   "qalora_group_size": 16,
-  "r": 32,
+  "r": 48,
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "mlp.Wo",
-    "attn.Wqkv",
     "attn.Wo",
-    "mlp.Wi"
+    "mlp.Wi",
+    "mlp.Wo",
+    "attn.Wqkv"
   ],
   "target_parameters": null,
   "task_type": "SEQ_CLS",

adapter_model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:45b8b3ab19c1d00413feebc69e4600e77324769c50d87348295f83f275f007e6
-size 27061816
+oid sha256:88d11d3ac7651728a68e3dd182b9db2a46e15599e84444a63252d6d7d0afbe4d
+size 40578664

jailbreak_type_mapping.json

@@ -1,10 +1 @@
-{
-  "label_to_idx": {
-    "benign": 0,
-    "jailbreak": 1
-  },
-  "idx_to_label": {
-    "0": "benign",
-    "1": "jailbreak"
-  }
-}
+{"label_to_id": {"benign": 0, "jailbreak": 1}, "id_to_label": {"0": "benign", "1": "jailbreak"}}

label_mapping.json

@@ -1,10 +1 @@
-{
-  "label_to_idx": {
-    "benign": 0,
-    "jailbreak": 1
-  },
-  "idx_to_label": {
-    "0": "benign",
-    "1": "jailbreak"
-  }
-}
+{"label_to_id": {"benign": 0, "jailbreak": 1}, "id_to_label": {"0": "benign", "1": "jailbreak"}}

lora_config.json

@@ -1 +1 @@
-{"rank": 32, "alpha": 64, "dropout": 0.1, "target_modules": ["attn.Wqkv", "attn.Wo", "mlp.Wi", "mlp.Wo"]}
+{"rank": 48, "alpha": 96, "dropout": 0.1, "target_modules": ["attn.Wqkv", "attn.Wo", "mlp.Wi", "mlp.Wo"]}