---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: microsoft/Phi-3-mini-128k-instruct
model-index:
- name: Bank-service
  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. -->

# Bank-service

This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co./microsoft/Phi-3-mini-128k-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5234

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4397        | 0.4   | 2    | 2.6366          |
| 2.5565        | 0.8   | 4    | 2.5772          |
| 2.2994        | 1.2   | 6    | 2.5455          |
| 2.472         | 1.6   | 8    | 2.5278          |
| 2.2456        | 2.0   | 10   | 2.5234          |


### Framework versions

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