---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: microsoft/phi-2
model-index:
- name: phi-2-prompt-injection-QLoRA
  results: []
datasets:
- HuggingFaceH4/no_robots
- Dahoas/synthetic-hh-rlhf-prompts
- HuggingFaceH4/ultrachat_200k
- Lakera/gandalf_ignore_instructions
- imoxto/prompt_injection_cleaned_dataset-v2
- hackaprompt/hackaprompt-dataset
- rubend18/ChatGPT-Jailbreak-Prompts
- HuggingFaceH4/instruction-dataset
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# phi-2-prompt-injection-QLoRA
Weights updated at 03/07/2024. (Training epochs increased, accuracy improved than before)

View training code: https://github.com/AIM-Intelligence/phi-2-prompt-injection-QLoRA

Try out the model in: https://aim-intelligence.com

This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0000
- eval_precision: 1.0
- eval_recall: 1.0
- eval_f1-score: 1.0
- eval_accuracy: 1.0
- eval_runtime: 16.0258
- eval_samples_per_second: 8.424
- eval_steps_per_second: 1.061
- step: 0

## Model description

More information needed

## Intended uses & limitations

```
tokenizer = AutoTokenizer.from_pretrained("ysy970923/phi-2-prompt-injection-QLoRA")
model = AutoModelForSequenceClassification.from_pretrained("ysy970923/phi-2-prompt-injection-QLoRA", load_in_4bit=True, torch_dtype=torch.bfloat16, id2label={0: "SAFE", 1: "INJECTION"})
# LABEL_0 is safe, LABEL_1 is prompt_injection
```

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0

### Framework versions

- PEFT 0.8.2
- Transformers 4.38.1
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2