---
library_name: peft
license: other
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: lora
  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. -->

# lora

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 flock_task4_tranning dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1333

## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 2
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1           | 0.0501 | 50   | 1.2970          |
| 1.0774        | 0.1002 | 100  | 1.2504          |
| 0.9563        | 0.1502 | 150  | 1.2521          |
| 1.0242        | 0.2003 | 200  | 1.2253          |
| 1.0038        | 0.2504 | 250  | 1.2113          |
| 0.9858        | 0.3005 | 300  | 1.1920          |
| 0.8694        | 0.3505 | 350  | 1.1918          |
| 0.9174        | 0.4006 | 400  | 1.1884          |
| 0.9653        | 0.4507 | 450  | 1.1870          |
| 0.9136        | 0.5008 | 500  | 1.1768          |
| 0.9014        | 0.5508 | 550  | 1.1673          |
| 0.9203        | 0.6009 | 600  | 1.1558          |
| 0.8902        | 0.6510 | 650  | 1.1679          |
| 0.9018        | 0.7011 | 700  | 1.1489          |
| 0.937         | 0.7511 | 750  | 1.1577          |
| 0.8984        | 0.8012 | 800  | 1.1463          |
| 0.8607        | 0.8513 | 850  | 1.1517          |
| 0.8698        | 0.9014 | 900  | 1.1436          |
| 0.9661        | 0.9514 | 950  | 1.1479          |
| 0.672         | 1.0010 | 1000 | 1.1459          |
| 0.8162        | 1.0511 | 1050 | 1.1374          |
| 0.8477        | 1.1012 | 1100 | 1.1434          |
| 0.9039        | 1.1512 | 1150 | 1.1394          |
| 0.8361        | 1.2013 | 1200 | 1.1434          |
| 0.8091        | 1.2514 | 1250 | 1.1391          |
| 0.7854        | 1.3015 | 1300 | 1.1392          |
| 0.7716        | 1.3515 | 1350 | 1.1403          |
| 0.8637        | 1.4016 | 1400 | 1.1337          |
| 0.8491        | 1.4517 | 1450 | 1.1392          |
| 0.9037        | 1.5018 | 1500 | 1.1325          |
| 0.8698        | 1.5518 | 1550 | 1.1371          |
| 0.7614        | 1.6019 | 1600 | 1.1332          |
| 0.7492        | 1.6520 | 1650 | 1.1350          |
| 0.8217        | 1.7021 | 1700 | 1.1321          |
| 0.8261        | 1.7521 | 1750 | 1.1323          |
| 0.8286        | 1.8022 | 1800 | 1.1325          |
| 0.8208        | 1.8523 | 1850 | 1.1335          |
| 0.7937        | 1.9024 | 1900 | 1.1338          |
| 0.837         | 1.9524 | 1950 | 1.1342          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0