Downloads · 30 days
0
G0521HMA26H7
Litzy619/G0521HMA26H7
G0521HMA26H7 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 gemma.
should probably proofread and complete it, then remove this comment. --
Access
Public
Updated May 22, 2024
Parameters
2.5B
10.1 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors10 GB · 100%
At a glance
- License
- gemma
- Access
- Public
- Created
- May 22, 2024
- Updated
- May 22, 2024
- SHA
- c9979def
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. -->
G0521HMA26H7
This model is a fine-tuned version of google/gemma-2b on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0990
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 |
|---|---|---|---|
| 1.7533 | 0.09 | 10 | 1.3592 |
| 0.9791 | 0.18 | 20 | 0.4708 |
| 0.2914 | 0.27 | 30 | 0.1584 |
| 0.1518 | 0.36 | 40 | 0.1514 |
| 0.1476 | 0.45 | 50 | 0.1479 |
| 0.1477 | 0.54 | 60 | 0.1477 |
| 0.146 | 0.63 | 70 | 0.1451 |
| 0.1467 | 0.73 | 80 | 0.1468 |
| 0.1387 | 0.82 | 90 | 0.1401 |
| 0.1342 | 0.91 | 100 | 0.1306 |
| 0.1353 | 1.0 | 110 | 0.1319 |
| 0.1235 | 1.09 | 120 | 0.1225 |
| 0.114 | 1.18 | 130 | 0.1180 |
| 0.1184 | 1.27 | 140 | 0.1166 |
| 0.1203 | 1.36 | 150 | 0.1177 |
| 0.1158 | 1.45 | 160 | 0.1090 |
| 0.1112 | 1.54 | 170 | 0.1058 |
| 0.1095 | 1.63 | 180 | 0.1067 |
| 0.11 | 1.72 | 190 | 0.1082 |
| 0.1109 | 1.81 | 200 | 0.1044 |
| 0.1118 | 1.9 | 210 | 0.1044 |
| 0.1072 | 1.99 | 220 | 0.1023 |
| 0.0934 | 2.08 | 230 | 0.1022 |
| 0.0938 | 2.18 | 240 | 0.1023 |
| 0.0896 | 2.27 | 250 | 0.1004 |
| 0.0917 | 2.36 | 260 | 0.1014 |
| 0.0909 | 2.45 | 270 | 0.1004 |
| 0.0848 | 2.54 | 280 | 0.0992 |
| 0.0833 | 2.63 | 290 | 0.0999 |
| 0.0845 | 2.72 | 300 | 0.0995 |
| 0.0872 | 2.81 | 310 | 0.0993 |
| 0.0895 | 2.9 | 320 | 0.0990 |
| 0.0872 | 2.99 | 330 | 0.0990 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.0
- License
- gemma
- Created
- May 22, 2024
- Updated
- May 22, 2024