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V0515HMA1H
Litzy619/V0515HMA1H
V0515HMA1H 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 16, 2024
Parameters
2.9B
12.4 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. -->
V0515HMA1H
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: 0.0607
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 |
|---|---|---|---|
| 2.0863 | 0.09 | 10 | 0.9184 |
| 0.3027 | 0.18 | 20 | 0.1319 |
| 0.1256 | 0.27 | 30 | 0.1034 |
| 0.1059 | 0.36 | 40 | 0.0856 |
| 0.0881 | 0.45 | 50 | 0.0799 |
| 0.1034 | 0.54 | 60 | 0.0912 |
| 0.0937 | 0.63 | 70 | 0.0813 |
| 0.0821 | 0.73 | 80 | 0.0783 |
| 0.0869 | 0.82 | 90 | 0.0773 |
| 0.0779 | 0.91 | 100 | 0.0714 |
| 0.0838 | 1.0 | 110 | 0.0749 |
| 0.075 | 1.09 | 120 | 0.0722 |
| 0.0718 | 1.18 | 130 | 0.0774 |
| 0.0679 | 1.27 | 140 | 0.0680 |
| 0.0647 | 1.36 | 150 | 0.0670 |
| 0.0703 | 1.45 | 160 | 0.0668 |
| 0.0668 | 1.54 | 170 | 0.0646 |
| 0.0691 | 1.63 | 180 | 0.0605 |
| 0.0628 | 1.72 | 190 | 0.0643 |
| 0.073 | 1.81 | 200 | 0.0615 |
| 0.0572 | 1.9 | 210 | 0.0646 |
| 0.0621 | 1.99 | 220 | 0.0626 |
| 0.0484 | 2.08 | 230 | 0.0659 |
| 0.0483 | 2.18 | 240 | 0.0623 |
| 0.0455 | 2.27 | 250 | 0.0631 |
| 0.0456 | 2.36 | 260 | 0.0617 |
| 0.0476 | 2.45 | 270 | 0.0600 |
| 0.0419 | 2.54 | 280 | 0.0614 |
| 0.0385 | 2.63 | 290 | 0.0643 |
| 0.0457 | 2.72 | 300 | 0.0624 |
| 0.0457 | 2.81 | 310 | 0.0614 |
| 0.0443 | 2.9 | 320 | 0.0609 |
| 0.0461 | 2.99 | 330 | 0.0607 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.0
- License
- mit
- Created
- May 16, 2024
- Updated
- May 16, 2024