---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: microsoft/Phi-3-mini-4k-instruct
model-index:
- name: phi-3-4k-instruct-domain-sft-1
  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-3-4k-instruct-domain-sft-1

This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9328

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.8958        | 0.1445 | 10   | 1.0716          |
| 1.7839        | 0.2890 | 20   | 1.0458          |
| 1.7146        | 0.4335 | 30   | 1.0222          |
| 1.6457        | 0.5780 | 40   | 1.0029          |
| 1.591         | 0.7225 | 50   | 0.9872          |
| 1.552         | 0.8670 | 60   | 0.9740          |
| 1.5115        | 1.0115 | 70   | 0.9631          |
| 1.4681        | 1.1560 | 80   | 0.9541          |
| 1.4469        | 1.3005 | 90   | 0.9468          |
| 1.419         | 1.4450 | 100  | 0.9412          |
| 1.4033        | 1.5895 | 110  | 0.9371          |
| 1.3928        | 1.7340 | 120  | 0.9343          |
| 1.3887        | 1.8785 | 130  | 0.9328          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1