peft safetensors trl sft generated_from_trainer base_model:microsoft/phi-2 license:mit region:us
---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: microsoft/phi-2
model-index:
- name: ca-finetuned-phi-2
  results: []
---

<!-- 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. -->

# ca-finetuned-phi-2

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:
- Loss: 1.5643
- Perplexity: 0.0000

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Perplexity |
|:-------------:|:-----:|:----:|:---------------:|:----------:|
| No log        | 0.97  | 7    | 2.2352          | 0.0000     |
| No log        | 1.95  | 14   | 2.0164          | 0.0000     |
| 2.4567        | 2.92  | 21   | 1.8511          | 0.0000     |
| 2.4567        | 3.9   | 28   | 1.7391          | 0.0000     |
| 1.8517        | 4.87  | 35   | 1.6667          | 0.0000     |
| 1.8517        | 5.98  | 43   | 1.6059          | 0.0000     |
| 1.5627        | 6.96  | 50   | 1.5816          | 0.0000     |
| 1.5627        | 7.93  | 57   | 1.5672          | 0.0000     |
| 1.41          | 8.9   | 64   | 1.5650          | 0.0000     |
| 1.41          | 9.74  | 70   | 1.5643          | 0.0000     |


### Framework versions

- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.1