---
base_model: microsoft/Phi-3.5-mini-instruct
library_name: peft
license: mit
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Phi-3.5-MultiCap
  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. -->

# Phi-3.5-MultiCap

This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5367

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0783        | 0.1354 | 30   | 1.0955          |
| 0.716         | 0.2707 | 60   | 0.7190          |
| 0.6167        | 0.4061 | 90   | 0.6266          |
| 0.6226        | 0.5415 | 120  | 0.5929          |
| 0.5665        | 0.6768 | 150  | 0.5737          |
| 0.5834        | 0.8122 | 180  | 0.5621          |
| 0.5931        | 0.9475 | 210  | 0.5549          |
| 0.5431        | 1.0829 | 240  | 0.5496          |
| 0.5678        | 1.2183 | 270  | 0.5458          |
| 0.5336        | 1.3536 | 300  | 0.5425          |
| 0.5292        | 1.4890 | 330  | 0.5403          |
| 0.5627        | 1.6244 | 360  | 0.5384          |
| 0.5493        | 1.7597 | 390  | 0.5374          |
| 0.5154        | 1.8951 | 420  | 0.5367          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.0+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1