---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: microsoft/Phi-3-mini-4k-instruct
model-index:
- name: outputs
  results: []
---

## Toxicity Classification Performance

Our merged model demonstrates exceptional performance on the toxicity classification task, outperforming several state-of-the-art language models.

### Classification Metrics

```
              precision    recall  f1-score   support

           0       0.85      0.90      0.87       175
           1       0.89      0.85      0.87       175

    accuracy                           0.87       350
   macro avg       0.87      0.87      0.87       350
weighted avg       0.87      0.87      0.87       350
```

Our model achieves an impressive precision of 0.85 for the toxic class and 0.89 for the non-toxic class, with a high overall accuracy of 0.87. The balanced F1-scores of 0.87 for both classes demonstrate the model's ability to handle this binary classification task effectively.

### Comparison with Other Models

| Model             | Precision | Recall | F1     |
|-------------------|----------:|-------:|-------:|
| Our Merged Model  | 0.85      | 0.90   | 0.87   |
| GPT-4             | 0.91      | 0.91   | 0.91   |
| GPT-4 Turbo       | 0.89      | 0.77   | 0.83   |
| Gemini Pro        | 0.81      | 0.84   | 0.83   |
| GPT-3.5 Turbo     | 0.93      | 0.83   | 0.87   |
| Palm              | -         | -      | -      |
| Claude V2         | -         | -      | -      |
[1] Scores from arize/phoenix

Compared to other language models, our merged model demonstrates competitive performance at a much smaller size, with a precision score of 0.85 and an F1 score of 0.87.

We will continue to refine and improve our merged model to achieve even better performance on model based toxicity evaluation tasks.

Citations: [1] https://docs.arize.com/phoenix/evaluation/how-to-evals/running-pre-tested-evals/retrieval-rag-relevance

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0009
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 110
- mixed_precision_training: Native AMP

### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1