---
base_model: microsoft/Phi-3-mini-4k-instruct
library_name: peft
license: mit
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: phi-3-mini-LoRA-MEDQA-Extended-V2
  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-mini-LoRA-MEDQA-Extended-V2

This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6515

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- 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: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7812        | 0.2205 | 100  | 0.6948          |
| 0.6796        | 0.4410 | 200  | 0.6695          |
| 0.6687        | 0.6615 | 300  | 0.6632          |
| 0.662         | 0.8820 | 400  | 0.6599          |
| 0.6603        | 1.1025 | 500  | 0.6579          |
| 0.6587        | 1.3230 | 600  | 0.6568          |
| 0.6539        | 1.5436 | 700  | 0.6553          |
| 0.6552        | 1.7641 | 800  | 0.6540          |
| 0.6542        | 1.9846 | 900  | 0.6537          |
| 0.6501        | 2.2051 | 1000 | 0.6526          |
| 0.6523        | 2.4256 | 1100 | 0.6521          |
| 0.6527        | 2.6461 | 1200 | 0.6517          |
| 0.6502        | 2.8666 | 1300 | 0.6515          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.43.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1