|
--- |
|
license: mit |
|
base_model: microsoft/Phi-3-mini-4k-instruct |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: PHI30511HMA9H |
|
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. --> |
|
|
|
# PHI30511HMA9H |
|
|
|
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: 0.0869 |
|
|
|
## 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: 100 |
|
- num_epochs: 3 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:----:|:---------------:| |
|
| 2.8775 | 0.09 | 10 | 0.3763 | |
|
| 0.2111 | 0.18 | 20 | 0.1599 | |
|
| 0.1716 | 0.27 | 30 | 0.1572 | |
|
| 0.1387 | 0.36 | 40 | 0.1256 | |
|
| 0.1207 | 0.45 | 50 | 0.1180 | |
|
| 0.122 | 0.54 | 60 | 0.0924 | |
|
| 0.0892 | 0.63 | 70 | 0.1051 | |
|
| 0.0987 | 0.73 | 80 | 0.0895 | |
|
| 0.0714 | 0.82 | 90 | 0.0755 | |
|
| 0.0719 | 0.91 | 100 | 0.0724 | |
|
| 0.0733 | 1.0 | 110 | 0.0718 | |
|
| 0.049 | 1.09 | 120 | 0.0710 | |
|
| 0.0504 | 1.18 | 130 | 0.0854 | |
|
| 0.0585 | 1.27 | 140 | 0.0735 | |
|
| 0.0539 | 1.36 | 150 | 0.0671 | |
|
| 0.0588 | 1.45 | 160 | 0.0735 | |
|
| 0.0502 | 1.54 | 170 | 0.0683 | |
|
| 0.0509 | 1.63 | 180 | 0.0710 | |
|
| 0.044 | 1.72 | 190 | 0.0674 | |
|
| 0.0467 | 1.81 | 200 | 0.0708 | |
|
| 0.0521 | 1.9 | 210 | 0.0689 | |
|
| 0.0468 | 1.99 | 220 | 0.0721 | |
|
| 0.0233 | 2.08 | 230 | 0.0698 | |
|
| 0.0207 | 2.18 | 240 | 0.0851 | |
|
| 0.0189 | 2.27 | 250 | 0.1004 | |
|
| 0.0112 | 2.36 | 260 | 0.1035 | |
|
| 0.0194 | 2.45 | 270 | 0.0972 | |
|
| 0.0133 | 2.54 | 280 | 0.0941 | |
|
| 0.0184 | 2.63 | 290 | 0.0909 | |
|
| 0.0207 | 2.72 | 300 | 0.0879 | |
|
| 0.0158 | 2.81 | 310 | 0.0870 | |
|
| 0.0192 | 2.9 | 320 | 0.0871 | |
|
| 0.015 | 2.99 | 330 | 0.0869 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.36.0.dev0 |
|
- Pytorch 2.1.2+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.14.1 |
|
|
