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G0519ABLATION1V2
Litzy619/G0519ABLATION1V2
G0519ABLATION1V2 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 19, 2024
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20.2 GB on disk
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At a glance
- License
- gemma
- Access
- Public
- Created
- May 19, 2024
- Updated
- May 19, 2024
- SHA
- 2ed5e87b
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. -->
G0519ABLATION1V2
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: 0.1258
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 |
|---|---|---|---|
| 2.9687 | 0.09 | 10 | 2.1590 |
| 1.5532 | 0.18 | 20 | 0.8435 |
| 0.4376 | 0.27 | 30 | 0.1905 |
| 0.1715 | 0.36 | 40 | 0.1689 |
| 0.1512 | 0.45 | 50 | 0.1495 |
| 0.1513 | 0.54 | 60 | 0.1516 |
| 0.1496 | 0.63 | 70 | 0.1478 |
| 0.1501 | 0.73 | 80 | 0.1485 |
| 0.1423 | 0.82 | 90 | 0.1490 |
| 0.1442 | 0.91 | 100 | 0.1474 |
| 0.1491 | 1.0 | 110 | 0.1487 |
| 0.1439 | 1.09 | 120 | 0.1490 |
| 0.1449 | 1.18 | 130 | 0.1481 |
| 0.1458 | 1.27 | 140 | 0.1467 |
| 0.1476 | 1.36 | 150 | 0.1463 |
| 0.1424 | 1.45 | 160 | 0.1486 |
| 0.1432 | 1.54 | 170 | 0.1453 |
| 0.144 | 1.63 | 180 | 0.1443 |
| 0.1455 | 1.72 | 190 | 0.1483 |
| 0.1451 | 1.81 | 200 | 0.1461 |
| 0.1451 | 1.9 | 210 | 0.1441 |
| 0.1443 | 1.99 | 220 | 0.1416 |
| 0.1386 | 2.08 | 230 | 0.1394 |
| 0.1337 | 2.18 | 240 | 0.1378 |
| 0.1344 | 2.27 | 250 | 0.1328 |
| 0.1336 | 2.36 | 260 | 0.1341 |
| 0.1312 | 2.45 | 270 | 0.1306 |
| 0.1245 | 2.54 | 280 | 0.1283 |
| 0.1239 | 2.63 | 290 | 0.1273 |
| 0.1247 | 2.72 | 300 | 0.1269 |
| 0.1252 | 2.81 | 310 | 0.1258 |
| 0.1248 | 2.9 | 320 | 0.1258 |
| 0.1263 | 2.99 | 330 | 0.1258 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.0
- License
- gemma
- Created
- May 19, 2024
- Updated
- May 19, 2024