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V0309P2
Litzy619/V0309P2
V0309P2 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 Mar 10, 2024
Parameters
2.8B
6.1 GB on disk
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.safetensors5.8 GB · 96%
Parameter types
How the weights are stored.
F162.8B · 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. -->
V0309P2
This model is a fine-tuned version of microsoft/phi-2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0699
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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- 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: 20
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.715 | 0.09 | 10 | 0.1736 |
| 0.1331 | 0.17 | 20 | 0.0929 |
| 0.1048 | 0.26 | 30 | 0.0795 |
| 0.0918 | 0.34 | 40 | 0.0688 |
| 0.0876 | 0.43 | 50 | 0.0683 |
| 0.0816 | 0.51 | 60 | 0.0639 |
| 0.0755 | 0.6 | 70 | 0.0607 |
| 0.0797 | 0.68 | 80 | 0.0603 |
| 0.068 | 0.77 | 90 | 0.0595 |
| 0.0652 | 0.85 | 100 | 0.0606 |
| 0.0713 | 0.94 | 110 | 0.0590 |
| 0.0684 | 1.02 | 120 | 0.0607 |
| 0.0576 | 1.11 | 130 | 0.0647 |
| 0.0554 | 1.19 | 140 | 0.0556 |
| 0.0538 | 1.28 | 150 | 0.0537 |
| 0.0515 | 1.37 | 160 | 0.0625 |
| 0.0532 | 1.45 | 170 | 0.0578 |
| 0.0481 | 1.54 | 180 | 0.0615 |
| 0.0519 | 1.62 | 190 | 0.0576 |
| 0.0548 | 1.71 | 200 | 0.0575 |
| 0.0541 | 1.79 | 210 | 0.0578 |
| 0.0481 | 1.88 | 220 | 0.0645 |
| 0.0478 | 1.96 | 230 | 0.0594 |
| 0.043 | 2.05 | 240 | 0.0607 |
| 0.0346 | 2.13 | 250 | 0.0659 |
| 0.031 | 2.22 | 260 | 0.0739 |
| 0.029 | 2.3 | 270 | 0.0767 |
| 0.0357 | 2.39 | 280 | 0.0749 |
| 0.0368 | 2.47 | 290 | 0.0713 |
| 0.0382 | 2.56 | 300 | 0.0684 |
| 0.0354 | 2.65 | 310 | 0.0685 |
| 0.0303 | 2.73 | 320 | 0.0689 |
| 0.0331 | 2.82 | 330 | 0.0696 |
| 0.0315 | 2.9 | 340 | 0.0700 |
| 0.0345 | 2.99 | 350 | 0.0699 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
- License
- mit
- Created
- Mar 10, 2024
- Updated
- Mar 10, 2024