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---
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
|