tinyllama_moe_sft_ultrachat-slimorca
This model is a fine-tuned version of ondevicellm/tinyllama_moe on the HuggingFaceH4/ultrachat_200k and the ondevicellm/SlimOrca datasets. It achieves the following results on the evaluation set:
- Loss: 1.1526
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: 120
- num_epochs: 1
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
1.4601 | 0.05 | 100 | 1.3361 |
1.3324 | 0.1 | 200 | 1.2566 |
1.2946 | 0.14 | 300 | 1.2279 |
1.2767 | 0.19 | 400 | 1.2111 |
1.2298 | 0.24 | 500 | 1.1995 |
1.2247 | 0.29 | 600 | 1.1902 |
1.2208 | 0.34 | 700 | 1.1833 |
1.2375 | 0.39 | 800 | 1.1775 |
1.2038 | 0.43 | 900 | 1.1726 |
1.1926 | 0.48 | 1000 | 1.1683 |
1.1933 | 0.53 | 1100 | 1.1649 |
1.1893 | 0.58 | 1200 | 1.1618 |
1.2029 | 0.63 | 1300 | 1.1593 |
1.2201 | 0.68 | 1400 | 1.1572 |
1.1741 | 0.72 | 1500 | 1.1557 |
1.1813 | 0.77 | 1600 | 1.1545 |
1.1668 | 0.82 | 1700 | 1.1536 |
1.1495 | 0.87 | 1800 | 1.1530 |
1.1595 | 0.92 | 1900 | 1.1527 |
1.1607 | 0.97 | 2000 | 1.1526 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0
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ondevicellm/tinyllama_moe