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Phi0503HMA12OLD
Litzy619/Phi0503HMA12OLD
Phi0503HMA12OLD 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 8, 2024
Parameters
3.9B
8.4 GB on disk
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Repo makeup
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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. -->
Phi0503HMA12
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: 0.1482
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 |
|---|---|---|---|
| 4.1436 | 0.09 | 10 | 0.9672 |
| 1.4616 | 0.18 | 20 | 0.2924 |
| 0.3027 | 0.27 | 30 | 0.3155 |
| 0.2571 | 0.36 | 40 | 0.5354 |
| 0.2363 | 0.45 | 50 | 0.1464 |
| 1.3304 | 0.54 | 60 | 4.0884 |
| 1.6152 | 0.63 | 70 | 0.2058 |
| 0.1811 | 0.73 | 80 | 0.2503 |
| 0.1621 | 0.82 | 90 | 1.4058 |
| 0.4746 | 0.91 | 100 | 6.5459 |
| 4.6694 | 1.0 | 110 | 2.2291 |
| 2.4032 | 1.09 | 120 | 1.3617 |
| 1.0099 | 1.18 | 130 | 0.8414 |
| 0.6647 | 1.27 | 140 | 0.4067 |
| 0.368 | 1.36 | 150 | 0.3385 |
| 0.3105 | 1.45 | 160 | 0.2820 |
| 0.2669 | 1.54 | 170 | 0.2006 |
| 0.1954 | 1.63 | 180 | 0.1815 |
| 0.2017 | 1.72 | 190 | 0.1772 |
| 0.1875 | 1.81 | 200 | 0.1799 |
| 0.181 | 1.9 | 210 | 0.1682 |
| 0.1678 | 1.99 | 220 | 0.1651 |
| 0.1623 | 2.08 | 230 | 0.1537 |
| 0.15 | 2.18 | 240 | 0.1502 |
| 0.1497 | 2.27 | 250 | 0.1529 |
| 0.1503 | 2.36 | 260 | 0.1496 |
| 0.1439 | 2.45 | 270 | 0.1488 |
| 0.1509 | 2.54 | 280 | 0.1489 |
| 0.1483 | 2.63 | 290 | 0.1494 |
| 0.1483 | 2.72 | 300 | 0.1483 |
| 0.1546 | 2.81 | 310 | 0.1483 |
| 0.1494 | 2.9 | 320 | 0.1483 |
| 0.1487 | 2.99 | 330 | 0.1482 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.0
- License
- mit
- Created
- May 7, 2024
- Updated
- May 8, 2024