---
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-mt-8
  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-mt-8

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: 0.6751

## 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: 8

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.005         | 0.5109 | 50   | 0.9799          |
| 0.7452        | 1.0217 | 100  | 0.7840          |
| 0.7469        | 1.5326 | 150  | 0.7372          |
| 0.7326        | 2.0434 | 200  | 0.7166          |
| 0.7264        | 2.5543 | 250  | 0.7045          |
| 0.7241        | 3.0651 | 300  | 0.6965          |
| 0.6842        | 3.5760 | 350  | 0.6910          |
| 0.6712        | 4.0868 | 400  | 0.6865          |
| 0.6824        | 4.5977 | 450  | 0.6831          |
| 0.6654        | 5.1086 | 500  | 0.6809          |
| 0.7041        | 5.6194 | 550  | 0.6791          |
| 0.6573        | 6.1303 | 600  | 0.6775          |
| 0.6606        | 6.6411 | 650  | 0.6762          |
| 0.6561        | 7.1520 | 700  | 0.6755          |
| 0.6799        | 7.6628 | 750  | 0.6751          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1