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V0309P6
Litzy619/V0309P6
V0309P6 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 Mar 10, 2024
Parameters
2.8B
6.1 GB on disk
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.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. -->
V0309P6
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: 0.0648
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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- 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: 20
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.969 | 0.09 | 10 | 0.5527 |
| 0.2118 | 0.17 | 20 | 0.0895 |
| 0.1076 | 0.26 | 30 | 0.0750 |
| 0.0998 | 0.34 | 40 | 0.0690 |
| 0.0936 | 0.43 | 50 | 0.0643 |
| 0.0846 | 0.51 | 60 | 0.0642 |
| 0.0784 | 0.6 | 70 | 0.0639 |
| 0.0857 | 0.68 | 80 | 0.0668 |
| 0.0748 | 0.77 | 90 | 0.0641 |
| 0.111 | 0.85 | 100 | 0.0680 |
| 0.0874 | 0.94 | 110 | 0.0704 |
| 0.0842 | 1.02 | 120 | 0.0675 |
| 0.0797 | 1.11 | 130 | 0.0678 |
| 0.0731 | 1.19 | 140 | 0.0642 |
| 0.0714 | 1.28 | 150 | 0.0584 |
| 0.0709 | 1.37 | 160 | 0.0621 |
| 0.0703 | 1.45 | 170 | 0.0587 |
| 0.0638 | 1.54 | 180 | 0.0595 |
| 0.0678 | 1.62 | 190 | 0.0580 |
| 0.067 | 1.71 | 200 | 0.0600 |
| 0.0672 | 1.79 | 210 | 0.0604 |
| 0.0627 | 1.88 | 220 | 0.0640 |
| 0.0587 | 1.96 | 230 | 0.0592 |
| 0.057 | 2.05 | 240 | 0.0622 |
| 0.0486 | 2.13 | 250 | 0.0663 |
| 0.0484 | 2.22 | 260 | 0.0690 |
| 0.0457 | 2.3 | 270 | 0.0677 |
| 0.0529 | 2.39 | 280 | 0.0636 |
| 0.0533 | 2.47 | 290 | 0.0622 |
| 0.0523 | 2.56 | 300 | 0.0627 |
| 0.0523 | 2.65 | 310 | 0.0638 |
| 0.0456 | 2.73 | 320 | 0.0642 |
| 0.048 | 2.82 | 330 | 0.0648 |
| 0.0454 | 2.9 | 340 | 0.0642 |
| 0.0491 | 2.99 | 350 | 0.0648 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
- License
- mit
- Created
- Mar 10, 2024
- Updated
- Mar 10, 2024