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V0503HMA22H
Litzy619/V0503HMA22H
V0503HMA22H 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. --
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Public
Updated May 12, 2024
Parameters
2.9B
6.5 GB on disk
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.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. -->
V0503HMA22H
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: 0.0674
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 |
|---|---|---|---|
| 1.8681 | 0.09 | 10 | 0.4172 |
| 0.2073 | 0.18 | 20 | 0.1180 |
| 0.1143 | 0.27 | 30 | 0.0880 |
| 0.0961 | 0.36 | 40 | 0.0789 |
| 0.0804 | 0.45 | 50 | 0.0744 |
| 0.0861 | 0.54 | 60 | 0.0761 |
| 0.0787 | 0.63 | 70 | 0.0696 |
| 0.0757 | 0.73 | 80 | 0.0854 |
| 0.0807 | 0.82 | 90 | 0.0686 |
| 0.0806 | 0.91 | 100 | 0.0697 |
| 0.0791 | 1.0 | 110 | 0.0647 |
| 0.0651 | 1.09 | 120 | 0.0673 |
| 0.063 | 1.18 | 130 | 0.0786 |
| 0.0623 | 1.27 | 140 | 0.0629 |
| 0.0638 | 1.36 | 150 | 0.0735 |
| 0.0739 | 1.45 | 160 | 0.0622 |
| 0.0593 | 1.54 | 170 | 0.0639 |
| 0.0675 | 1.63 | 180 | 0.0626 |
| 0.0555 | 1.72 | 190 | 0.0615 |
| 0.068 | 1.81 | 200 | 0.0609 |
| 0.0555 | 1.9 | 210 | 0.0609 |
| 0.0503 | 1.99 | 220 | 0.0582 |
| 0.0366 | 2.08 | 230 | 0.0591 |
| 0.0334 | 2.18 | 240 | 0.0705 |
| 0.0294 | 2.27 | 250 | 0.0722 |
| 0.0296 | 2.36 | 260 | 0.0685 |
| 0.0369 | 2.45 | 270 | 0.0674 |
| 0.0303 | 2.54 | 280 | 0.0682 |
| 0.0286 | 2.63 | 290 | 0.0684 |
| 0.0312 | 2.72 | 300 | 0.0680 |
| 0.0323 | 2.81 | 310 | 0.0675 |
| 0.0304 | 2.9 | 320 | 0.0674 |
| 0.0341 | 2.99 | 330 | 0.0674 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
- License
- mit
- Created
- May 10, 2024
- Updated
- May 12, 2024