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V0507HMA15HV3
Litzy619/V0507HMA15HV3
V0507HMA15HV3 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
2.9B
6.5 GB on disk
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.safetensors5.9 GB · 91%
Parameter types
How the weights are stored.
F162.8B · 97%
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. -->
V0507HMA15HV3
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: 0.0511
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 |
|---|---|---|---|
| 1.9302 | 0.09 | 10 | 0.6932 |
| 0.2793 | 0.18 | 20 | 0.1168 |
| 0.1139 | 0.27 | 30 | 0.0926 |
| 0.0965 | 0.36 | 40 | 0.0849 |
| 0.0863 | 0.45 | 50 | 0.0783 |
| 0.0925 | 0.54 | 60 | 0.0724 |
| 0.0781 | 0.63 | 70 | 0.0691 |
| 0.0773 | 0.73 | 80 | 0.0701 |
| 0.077 | 0.82 | 90 | 0.0726 |
| 0.0778 | 0.91 | 100 | 0.0682 |
| 0.0767 | 1.0 | 110 | 0.0652 |
| 0.0604 | 1.09 | 120 | 0.0701 |
| 0.0669 | 1.18 | 130 | 0.0701 |
| 0.0642 | 1.27 | 140 | 0.0682 |
| 0.0648 | 1.36 | 150 | 0.0742 |
| 0.0722 | 1.45 | 160 | 0.0779 |
| 0.0734 | 1.54 | 170 | 0.0690 |
| 0.0741 | 1.63 | 180 | 0.0717 |
| 0.0708 | 1.72 | 190 | 0.0667 |
| 0.0713 | 1.81 | 200 | 0.0723 |
| 0.0594 | 1.9 | 210 | 0.0629 |
| 0.0572 | 1.99 | 220 | 0.0589 |
| 0.0405 | 2.08 | 230 | 0.0625 |
| 0.0339 | 2.18 | 240 | 0.0483 |
| 0.0298 | 2.27 | 250 | 0.0565 |
| 0.022 | 2.36 | 260 | 0.0620 |
| 0.025 | 2.45 | 270 | 0.0513 |
| 0.0259 | 2.54 | 280 | 0.0502 |
| 0.0236 | 2.63 | 290 | 0.0509 |
| 0.0207 | 2.72 | 300 | 0.0506 |
| 0.027 | 2.81 | 310 | 0.0511 |
| 0.0237 | 2.9 | 320 | 0.0511 |
| 0.0238 | 2.99 | 330 | 0.0511 |
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 8, 2024