This is a fine-tuned model on the dataset of prompts to classify whether a prompt is malicious or benign. It is designed to be used in safety filters and moderation tools.

Developed by: Amina Akhtar
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Uses

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Bias, Risks, and Limitations

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Recommendations

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

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_id ="AminaAkhtar/Llama-3.2-1B-prompt-classifier"
label_map = {0:"benign", 1:"malicious"}
MAX_LENGTH=256
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModelForSequenceClassification.from_pretrained(
    model_id,
    num_labels=len(label_map)
).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
model.eval() # set to evaluation mode

def classify_prompt(user_input):
    inputs = tokenizer(user_input, return_tensors="pt", truncation=True,padding=False,max_length=MAX_LENGTH)
    inputs = {k: v.to(model.device) for k, v in inputs.items()}
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.nn.functional.softmax(logits, dim=-1)
    predicted_class = torch.argmax(probs, dim=-1).item()
    confidence = probs[0, predicted_class].item()
    predicted_label = "Benign" if predicted_class == 0 else "Malicious"
    return {
        "Label":predicted_label,
        "Confidence":confidence
        }

classify_prompt("<user_input>")        

Training Details

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Training Procedure

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Evaluation

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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