Downloads · 30 days
0
G0514BMAb
Litzy619/G0514BMAb
G0514BMAb 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 15, 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 14, 2024
- Updated
- May 15, 2024
- SHA
- 1a7d4a7f
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. -->
G0514BMAb
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: 0.1185
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 |
|---|---|---|---|
| 1.8905 | 0.09 | 10 | 1.6966 |
| 1.4604 | 0.18 | 20 | 1.1261 |
| 0.8483 | 0.27 | 30 | 0.5044 |
| 0.3447 | 0.36 | 40 | 0.1967 |
| 0.165 | 0.45 | 50 | 0.1540 |
| 0.1498 | 0.54 | 60 | 0.1496 |
| 0.1475 | 0.63 | 70 | 0.1463 |
| 0.1476 | 0.73 | 80 | 0.1468 |
| 0.1418 | 0.82 | 90 | 0.1477 |
| 0.1431 | 0.91 | 100 | 0.1442 |
| 0.1456 | 1.0 | 110 | 0.1447 |
| 0.1402 | 1.09 | 120 | 0.1412 |
| 0.1376 | 1.18 | 130 | 0.1414 |
| 0.1368 | 1.27 | 140 | 0.1368 |
| 0.1411 | 1.36 | 150 | 0.1345 |
| 0.1333 | 1.45 | 160 | 0.1380 |
| 0.1334 | 1.54 | 170 | 0.1329 |
| 0.1313 | 1.63 | 180 | 0.1308 |
| 0.1318 | 1.72 | 190 | 0.1319 |
| 0.1279 | 1.81 | 200 | 0.1272 |
| 0.1299 | 1.9 | 210 | 0.1249 |
| 0.1269 | 1.99 | 220 | 0.1258 |
| 0.1197 | 2.08 | 230 | 0.1209 |
| 0.1226 | 2.18 | 240 | 0.1210 |
| 0.1166 | 2.27 | 250 | 0.1218 |
| 0.1241 | 2.36 | 260 | 0.1214 |
| 0.116 | 2.45 | 270 | 0.1210 |
| 0.1129 | 2.54 | 280 | 0.1202 |
| 0.1142 | 2.63 | 290 | 0.1193 |
| 0.1154 | 2.72 | 300 | 0.1187 |
| 0.1165 | 2.81 | 310 | 0.1184 |
| 0.1172 | 2.9 | 320 | 0.1184 |
| 0.1187 | 2.99 | 330 | 0.1185 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.0
- License
- gemma
- Created
- May 14, 2024
- Updated
- May 15, 2024