---
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Phi-3-mini-4k-instruct-mbti
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zmhzmh/huggingface/runs/h3uz5tey)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zmhzmh/huggingface/runs/h3uz5tey)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zmhzmh/huggingface/runs/h3uz5tey)
# Phi-3-mini-4k-instruct-mbti

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: 0.6615
- Accuracy: 0.6220

## 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: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.7155        | 0.1977 | 500  | 0.7196          | 0.5898   |
| 0.6873        | 0.3955 | 1000 | 0.6776          | 0.5931   |
| 0.6841        | 0.5932 | 1500 | 0.6620          | 0.6058   |
| 0.6746        | 0.7909 | 2000 | 0.6615          | 0.6220   |
| 0.6655        | 0.9886 | 2500 | 0.6647          | 0.6133   |
| 0.6092        | 1.1864 | 3000 | 0.6873          | 0.5716   |
| 0.5661        | 1.3841 | 3500 | 0.7262          | 0.6092   |
| 0.5565        | 1.5818 | 4000 | 0.6938          | 0.6185   |
| 0.5308        | 1.7795 | 4500 | 0.7100          | 0.6060   |
| 0.5236        | 1.9773 | 5000 | 0.7046          | 0.6127   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1