---
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-V3
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# phi-3-mini-LoRA-MEDQA-V3

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: 0.6086

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.6898        | 0.3633 | 100  | 0.6195          |
| 0.6113        | 0.7266 | 200  | 0.6134          |
| 0.6095        | 1.0899 | 300  | 0.6110          |
| 0.6034        | 1.4532 | 400  | 0.6103          |
| 0.6037        | 1.8165 | 500  | 0.6093          |
| 0.6043        | 2.1798 | 600  | 0.6089          |
| 0.5986        | 2.5431 | 700  | 0.6089          |
| 0.5993        | 2.9064 | 800  | 0.6086          |


### Framework versions

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