Downloads · 30 days
0
V0415MA2
Litzy619/V0415MA2
V0415MA2 is a machine learning model from Litzy619. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
Access
Public
Updated Apr 16, 2024
Parameters
2.8B
6.1 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors5.8 GB · 96%
Parameter types
How the weights are stored.
F162.8B · 98%
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
V0415MA2
This model is a fine-tuned version of microsoft/phi-2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0650
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: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.2555 | 0.09 | 10 | 1.0684 |
| 0.4718 | 0.18 | 20 | 0.1179 |
| 0.1156 | 0.27 | 30 | 0.0892 |
| 0.0957 | 0.36 | 40 | 0.0782 |
| 0.0806 | 0.45 | 50 | 0.0723 |
| 0.0828 | 0.54 | 60 | 0.0704 |
| 0.0745 | 0.63 | 70 | 0.0687 |
| 0.0737 | 0.73 | 80 | 0.0682 |
| 0.0753 | 0.82 | 90 | 0.0633 |
| 0.0729 | 0.91 | 100 | 0.0590 |
| 0.0679 | 1.0 | 110 | 0.0632 |
| 0.057 | 1.09 | 120 | 0.0626 |
| 0.0612 | 1.18 | 130 | 0.0616 |
| 0.0559 | 1.27 | 140 | 0.0655 |
| 0.0509 | 1.36 | 150 | 0.0605 |
| 0.0591 | 1.45 | 160 | 0.0594 |
| 0.0563 | 1.54 | 170 | 0.0590 |
| 0.0543 | 1.63 | 180 | 0.0561 |
| 0.0503 | 1.72 | 190 | 0.0592 |
| 0.0593 | 1.81 | 200 | 0.0565 |
| 0.048 | 1.9 | 210 | 0.0579 |
| 0.047 | 1.99 | 220 | 0.0633 |
| 0.0361 | 2.08 | 230 | 0.0606 |
| 0.0366 | 2.18 | 240 | 0.0635 |
| 0.0314 | 2.27 | 250 | 0.0656 |
| 0.031 | 2.36 | 260 | 0.0672 |
| 0.0348 | 2.45 | 270 | 0.0679 |
| 0.0317 | 2.54 | 280 | 0.0671 |
| 0.0299 | 2.63 | 290 | 0.0665 |
| 0.0361 | 2.72 | 300 | 0.0655 |
| 0.0351 | 2.81 | 310 | 0.0651 |
| 0.0334 | 2.9 | 320 | 0.0649 |
| 0.0371 | 2.99 | 330 | 0.0650 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
- License
- mit
- Created
- Apr 15, 2024
- Updated
- Apr 16, 2024