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Phi0503HMA3
Litzy619/Phi0503HMA3
Phi0503HMA3 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 4, 2024
Parameters
3.9B
16.7 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. -->
Phi0503HMA3
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: 0.0755
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 |
|---|---|---|---|
| 4.2281 | 0.09 | 10 | 0.6893 |
| 0.3554 | 0.18 | 20 | 0.2337 |
| 0.2494 | 0.27 | 30 | 0.2261 |
| 0.2206 | 0.36 | 40 | 0.1916 |
| 0.213 | 0.45 | 50 | 0.1778 |
| 0.1546 | 0.54 | 60 | 0.1014 |
| 0.1079 | 0.63 | 70 | 0.0987 |
| 0.0823 | 0.73 | 80 | 0.0974 |
| 0.0902 | 0.82 | 90 | 0.0855 |
| 0.0772 | 0.91 | 100 | 0.0706 |
| 0.076 | 1.0 | 110 | 0.0844 |
| 0.0666 | 1.09 | 120 | 0.0719 |
| 0.0634 | 1.18 | 130 | 0.0803 |
| 0.0711 | 1.27 | 140 | 0.0697 |
| 0.0638 | 1.36 | 150 | 0.0679 |
| 0.0665 | 1.45 | 160 | 0.0687 |
| 0.0635 | 1.54 | 170 | 0.0664 |
| 0.0605 | 1.63 | 180 | 0.0674 |
| 0.0554 | 1.72 | 190 | 0.0641 |
| 0.0604 | 1.81 | 200 | 0.0623 |
| 0.0567 | 1.9 | 210 | 0.0664 |
| 0.0528 | 1.99 | 220 | 0.0693 |
| 0.0327 | 2.08 | 230 | 0.0751 |
| 0.0273 | 2.18 | 240 | 0.0921 |
| 0.0225 | 2.27 | 250 | 0.0998 |
| 0.0254 | 2.36 | 260 | 0.0898 |
| 0.0331 | 2.45 | 270 | 0.0737 |
| 0.021 | 2.54 | 280 | 0.0749 |
| 0.0256 | 2.63 | 290 | 0.0767 |
| 0.0274 | 2.72 | 300 | 0.0765 |
| 0.0299 | 2.81 | 310 | 0.0760 |
| 0.0242 | 2.9 | 320 | 0.0754 |
| 0.0273 | 2.99 | 330 | 0.0755 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
- License
- mit
- Created
- May 4, 2024
- Updated
- May 4, 2024