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Phi0503HMA15
Litzy619/Phi0503HMA15
Phi0503HMA15 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 10, 2024
Parameters
3.9B
8.4 GB on disk
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.safetensors7.9 GB · 95%
Parameter types
How the weights are stored.
BF163.8B · 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. -->
Phi0503HMA15
This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0616
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 |
|---|---|---|---|
| 4.4844 | 0.09 | 10 | 0.8736 |
| 0.3931 | 0.18 | 20 | 0.2417 |
| 0.2471 | 0.27 | 30 | 0.2360 |
| 0.2276 | 0.36 | 40 | 0.2169 |
| 0.2082 | 0.45 | 50 | 0.1854 |
| 0.2197 | 0.54 | 60 | 0.2227 |
| 0.2251 | 0.63 | 70 | 0.2017 |
| 0.2114 | 0.73 | 80 | 0.2120 |
| 0.2076 | 0.82 | 90 | 0.1874 |
| 0.1868 | 0.91 | 100 | 0.1686 |
| 0.1725 | 1.0 | 110 | 0.1657 |
| 0.163 | 1.09 | 120 | 0.1645 |
| 0.164 | 1.18 | 130 | 0.1647 |
| 0.1737 | 1.27 | 140 | 0.1626 |
| 0.1665 | 1.36 | 150 | 0.1629 |
| 0.1662 | 1.45 | 160 | 0.1651 |
| 0.1425 | 1.54 | 170 | 0.0893 |
| 0.1374 | 1.63 | 180 | 0.0857 |
| 0.129 | 1.72 | 190 | 0.1095 |
| 0.0855 | 1.81 | 200 | 0.0848 |
| 0.0678 | 1.9 | 210 | 0.0823 |
| 0.0723 | 1.99 | 220 | 0.0818 |
| 0.062 | 2.08 | 230 | 0.0671 |
| 0.0583 | 2.18 | 240 | 0.0690 |
| 0.0553 | 2.27 | 250 | 0.0685 |
| 0.0511 | 2.36 | 260 | 0.0654 |
| 0.0528 | 2.45 | 270 | 0.0659 |
| 0.0493 | 2.54 | 280 | 0.0657 |
| 0.0479 | 2.63 | 290 | 0.0650 |
| 0.0483 | 2.72 | 300 | 0.0642 |
| 0.0598 | 2.81 | 310 | 0.0628 |
| 0.0528 | 2.9 | 320 | 0.0615 |
| 0.0529 | 2.99 | 330 | 0.0616 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.0
- License
- mit
- Created
- May 10, 2024
- Updated
- May 10, 2024