tinyllama_moe_sft_ultrachat200k_v2_epochs3
This model is a fine-tuned version of ondevicellm/tinyllama_moe on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set:
- Loss: 1.1178
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 115
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.336 | 0.09 | 100 | 1.3129 |
| 1.2424 | 0.18 | 200 | 1.2363 |
| 1.2079 | 0.26 | 300 | 1.2084 |
| 1.185 | 0.35 | 400 | 1.1911 |
| 1.1546 | 0.44 | 500 | 1.1787 |
| 1.1741 | 0.53 | 600 | 1.1692 |
| 1.1612 | 0.61 | 700 | 1.1613 |
| 1.1453 | 0.7 | 800 | 1.1547 |
| 1.141 | 0.79 | 900 | 1.1489 |
| 1.1247 | 0.88 | 1000 | 1.1438 |
| 1.1485 | 0.96 | 1100 | 1.1392 |
| 1.067 | 1.05 | 1200 | 1.1387 |
| 1.0694 | 1.14 | 1300 | 1.1368 |
| 1.0814 | 1.23 | 1400 | 1.1341 |
| 1.0727 | 1.31 | 1500 | 1.1316 |
| 1.0769 | 1.4 | 1600 | 1.1292 |
| 1.0728 | 1.49 | 1700 | 1.1270 |
| 1.0558 | 1.58 | 1800 | 1.1247 |
| 1.0753 | 1.66 | 1900 | 1.1229 |
| 1.0799 | 1.75 | 2000 | 1.1209 |
| 1.066 | 1.84 | 2100 | 1.1192 |
| 1.0406 | 1.93 | 2200 | 1.1178 |
| 1.0193 | 2.01 | 2300 | 1.1222 |
| 1.0276 | 2.1 | 2400 | 1.1220 |
| 1.0171 | 2.19 | 2500 | 1.1215 |
| 1.0112 | 2.28 | 2600 | 1.1211 |
| 1.0087 | 2.37 | 2700 | 1.1207 |
| 1.0158 | 2.45 | 2800 | 1.1204 |
| 1.0219 | 2.54 | 2900 | 1.1199 |
| 1.0024 | 2.63 | 3000 | 1.1197 |
| 1.019 | 2.72 | 3100 | 1.1197 |
| 1.0135 | 2.8 | 3200 | 1.1194 |
| 1.0094 | 2.89 | 3300 | 1.1194 |
| 1.0284 | 2.98 | 3400 | 1.1194 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.14.6
- Tokenizers 0.15.0