metadata
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- generated_from_trainer
model-index:
- name: PHI30512HMAB26H
results: []
PHI30512HMAB26H
This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0713
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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
4.8606 | 0.09 | 10 | 1.2529 |
0.634 | 0.18 | 20 | 0.2571 |
0.2918 | 0.27 | 30 | 0.2613 |
0.26 | 0.36 | 40 | 0.2799 |
0.2618 | 0.45 | 50 | 0.2381 |
0.2224 | 0.54 | 60 | 0.2114 |
0.2238 | 0.63 | 70 | 0.2278 |
0.2084 | 0.73 | 80 | 0.1813 |
0.1347 | 0.82 | 90 | 0.1057 |
0.0992 | 0.91 | 100 | 0.1193 |
0.0989 | 1.0 | 110 | 0.0853 |
0.0869 | 1.09 | 120 | 0.0838 |
0.0762 | 1.18 | 130 | 0.0755 |
0.0791 | 1.27 | 140 | 0.0743 |
0.0777 | 1.36 | 150 | 0.0775 |
0.0797 | 1.45 | 160 | 0.0712 |
0.0729 | 1.54 | 170 | 0.0685 |
0.0705 | 1.63 | 180 | 0.0706 |
0.0672 | 1.72 | 190 | 0.0751 |
0.0734 | 1.81 | 200 | 0.0688 |
0.0646 | 1.9 | 210 | 0.0709 |
0.0596 | 1.99 | 220 | 0.0763 |
0.0421 | 2.08 | 230 | 0.0864 |
0.0425 | 2.18 | 240 | 0.0845 |
0.0358 | 2.27 | 250 | 0.0775 |
0.0343 | 2.36 | 260 | 0.0763 |
0.0449 | 2.45 | 270 | 0.0717 |
0.0323 | 2.54 | 280 | 0.0723 |
0.0319 | 2.63 | 290 | 0.0724 |
0.0369 | 2.72 | 300 | 0.0727 |
0.0402 | 2.81 | 310 | 0.0717 |
0.0358 | 2.9 | 320 | 0.0713 |
0.0369 | 2.99 | 330 | 0.0713 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.0