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V0508HMA15HPHI3B2
Litzy619/V0508HMA15HPHI3B2
V0508HMA15HPHI3B2 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
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3.9B
8.3 GB on disk
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.safetensors8 GB · 96%
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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. -->
V0508HMA15HPHI3B2
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: 0.0885
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 |
|---|---|---|---|
| 2.3691 | 0.09 | 10 | 0.1863 |
| 0.1586 | 0.18 | 20 | 0.1406 |
| 0.1444 | 0.27 | 30 | 0.1391 |
| 0.1366 | 0.36 | 40 | 0.1237 |
| 0.1228 | 0.45 | 50 | 0.1353 |
| 0.1212 | 0.54 | 60 | 0.0898 |
| 0.1116 | 0.63 | 70 | 0.1008 |
| 0.0983 | 0.73 | 80 | 0.0803 |
| 0.0756 | 0.82 | 90 | 0.0930 |
| 0.0848 | 0.91 | 100 | 0.0721 |
| 0.073 | 1.0 | 110 | 0.0729 |
| 0.0499 | 1.09 | 120 | 0.0691 |
| 0.0594 | 1.18 | 130 | 0.1105 |
| 0.067 | 1.27 | 140 | 0.0751 |
| 0.0489 | 1.36 | 150 | 0.0821 |
| 0.0622 | 1.45 | 160 | 0.0838 |
| 0.0654 | 1.54 | 170 | 0.0764 |
| 0.0574 | 1.63 | 180 | 0.0826 |
| 0.0562 | 1.72 | 190 | 0.0757 |
| 0.0608 | 1.81 | 200 | 0.0795 |
| 0.061 | 1.9 | 210 | 0.0796 |
| 0.0552 | 1.99 | 220 | 0.0796 |
| 0.0293 | 2.08 | 230 | 0.0849 |
| 0.0219 | 2.18 | 240 | 0.1143 |
| 0.0257 | 2.27 | 250 | 0.0967 |
| 0.0204 | 2.36 | 260 | 0.0831 |
| 0.0251 | 2.45 | 270 | 0.0882 |
| 0.0182 | 2.54 | 280 | 0.0959 |
| 0.0189 | 2.63 | 290 | 0.0925 |
| 0.0243 | 2.72 | 300 | 0.0909 |
| 0.0226 | 2.81 | 310 | 0.0890 |
| 0.017 | 2.9 | 320 | 0.0884 |
| 0.0201 | 2.99 | 330 | 0.0885 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
- License
- mit
- Created
- May 8, 2024
- Updated
- May 9, 2024