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V0309P1
Litzy619/V0309P1
V0309P1 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
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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. -->
V0309P1
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: 0.0820
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 |
|---|---|---|---|
| 1.4262 | 0.09 | 10 | 0.1204 |
| 0.1236 | 0.17 | 20 | 0.0907 |
| 0.1031 | 0.26 | 30 | 0.0766 |
| 0.0896 | 0.34 | 40 | 0.0691 |
| 0.0871 | 0.43 | 50 | 0.0719 |
| 0.0821 | 0.51 | 60 | 0.0751 |
| 0.0749 | 0.6 | 70 | 0.0676 |
| 0.0809 | 0.68 | 80 | 0.0624 |
| 0.068 | 0.77 | 90 | 0.0591 |
| 0.062 | 0.85 | 100 | 0.0666 |
| 0.0712 | 0.94 | 110 | 0.0643 |
| 0.0679 | 1.02 | 120 | 0.0600 |
| 0.0488 | 1.11 | 130 | 0.0758 |
| 0.0498 | 1.19 | 140 | 0.0573 |
| 0.0451 | 1.28 | 150 | 0.0649 |
| 0.0434 | 1.37 | 160 | 0.0692 |
| 0.0449 | 1.45 | 170 | 0.0639 |
| 0.0401 | 1.54 | 180 | 0.0697 |
| 0.0477 | 1.62 | 190 | 0.0633 |
| 0.0492 | 1.71 | 200 | 0.0609 |
| 0.0489 | 1.79 | 210 | 0.0632 |
| 0.0422 | 1.88 | 220 | 0.0679 |
| 0.0417 | 1.96 | 230 | 0.0633 |
| 0.034 | 2.05 | 240 | 0.0678 |
| 0.0247 | 2.13 | 250 | 0.0700 |
| 0.0234 | 2.22 | 260 | 0.0766 |
| 0.0187 | 2.3 | 270 | 0.0816 |
| 0.0231 | 2.39 | 280 | 0.0841 |
| 0.0245 | 2.47 | 290 | 0.0859 |
| 0.024 | 2.56 | 300 | 0.0848 |
| 0.0253 | 2.65 | 310 | 0.0847 |
| 0.0202 | 2.73 | 320 | 0.0841 |
| 0.0242 | 2.82 | 330 | 0.0814 |
| 0.0187 | 2.9 | 340 | 0.0820 |
| 0.0217 | 2.99 | 350 | 0.0820 |
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