Downloads · 30 days
0
V0424HMA9
Litzy619/V0424HMA9
V0424HMA9 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 25, 2024
Parameters
2.9B
6.5 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors5.9 GB · 91%
Parameter types
How the weights are stored.
F162.8B · 97%
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. -->
V0424HMA9
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: 0.0624
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 |
|---|---|---|---|
| 1.7629 | 0.09 | 10 | 0.3668 |
| 0.1867 | 0.18 | 20 | 0.1122 |
| 0.1113 | 0.27 | 30 | 0.0923 |
| 0.1065 | 0.36 | 40 | 0.0843 |
| 0.081 | 0.45 | 50 | 0.0724 |
| 0.1068 | 0.54 | 60 | 0.0807 |
| 0.0797 | 0.63 | 70 | 0.0752 |
| 0.0773 | 0.73 | 80 | 0.0826 |
| 0.0898 | 0.82 | 90 | 0.0796 |
| 0.0923 | 0.91 | 100 | 0.0766 |
| 0.0803 | 1.0 | 110 | 0.0688 |
| 0.0663 | 1.09 | 120 | 0.0683 |
| 0.0629 | 1.18 | 130 | 0.0847 |
| 0.073 | 1.27 | 140 | 0.0767 |
| 0.0691 | 1.36 | 150 | 0.0683 |
| 0.0769 | 1.45 | 160 | 0.0649 |
| 0.0648 | 1.54 | 170 | 0.0673 |
| 0.0697 | 1.63 | 180 | 0.0685 |
| 0.0622 | 1.72 | 190 | 0.0604 |
| 0.0677 | 1.81 | 200 | 0.0656 |
| 0.0571 | 1.9 | 210 | 0.0620 |
| 0.0534 | 1.99 | 220 | 0.0579 |
| 0.0382 | 2.08 | 230 | 0.0640 |
| 0.036 | 2.18 | 240 | 0.0711 |
| 0.0345 | 2.27 | 250 | 0.0664 |
| 0.0303 | 2.36 | 260 | 0.0660 |
| 0.0354 | 2.45 | 270 | 0.0670 |
| 0.0336 | 2.54 | 280 | 0.0653 |
| 0.0318 | 2.63 | 290 | 0.0620 |
| 0.0322 | 2.72 | 300 | 0.0622 |
| 0.035 | 2.81 | 310 | 0.0627 |
| 0.0332 | 2.9 | 320 | 0.0626 |
| 0.0344 | 2.99 | 330 | 0.0624 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
- License
- mit
- Created
- Apr 24, 2024
- Updated
- Apr 25, 2024