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Phi0503HMA17OLD
Litzy619/Phi0503HMA17OLD
Phi0503HMA17OLD 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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Updated May 9, 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. -->
Phi0503HMA17
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: 0.1875
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: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.75 | 0.09 | 10 | 0.5453 |
| 0.3378 | 0.18 | 20 | 0.2824 |
| 0.3447 | 0.27 | 30 | 0.3174 |
| 0.2287 | 0.36 | 40 | 0.1820 |
| 0.1614 | 0.45 | 50 | 0.1595 |
| 0.1573 | 0.54 | 60 | 0.1415 |
| 0.1433 | 0.63 | 70 | 0.1354 |
| 0.2011 | 0.73 | 80 | 1.2273 |
| 0.311 | 0.82 | 90 | 0.1241 |
| 0.1326 | 0.91 | 100 | 0.1242 |
| 0.129 | 1.0 | 110 | 0.1278 |
| 0.0812 | 1.09 | 120 | 0.0927 |
| 0.1272 | 1.18 | 130 | 0.2229 |
| 3.2223 | 1.27 | 140 | 2.3282 |
| 1.9049 | 1.36 | 150 | 1.0767 |
| 0.8589 | 1.45 | 160 | 0.6321 |
| 0.5045 | 1.54 | 170 | 0.4553 |
| 0.5118 | 1.63 | 180 | 0.3732 |
| 0.3558 | 1.72 | 190 | 0.3349 |
| 0.3136 | 1.81 | 200 | 0.3106 |
| 0.3007 | 1.9 | 210 | 0.2946 |
| 0.3073 | 1.99 | 220 | 0.2602 |
| 0.3275 | 2.08 | 230 | 0.4014 |
| 0.5342 | 2.18 | 240 | 0.4574 |
| 0.3191 | 2.27 | 250 | 0.3423 |
| 0.2647 | 2.36 | 260 | 0.2386 |
| 0.23 | 2.45 | 270 | 0.2366 |
| 0.2493 | 2.54 | 280 | 0.2132 |
| 0.2129 | 2.63 | 290 | 0.2020 |
| 0.2034 | 2.72 | 300 | 0.1892 |
| 0.225 | 2.81 | 310 | 0.1930 |
| 0.1989 | 2.9 | 320 | 0.1899 |
| 0.1969 | 2.99 | 330 | 0.1875 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.0
- License
- mit
- Created
- May 9, 2024
- Updated
- May 9, 2024