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qlora-adapter-Llama-2-7b-hf-databricks-dolly-15k
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the databricks/databricks-dolly-15k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1313
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Trained on RTX A5000 - 24GB GPU. The training took 3 hours 31 mins on the datasets with 12008 train samples and 1501 validation samples
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.1584 | 0.08 | 1000 | 1.1782 |
| 1.0667 | 0.17 | 2000 | 1.1710 |
| 1.0662 | 0.25 | 3000 | 1.1599 |
| 1.0517 | 0.33 | 4000 | 1.1569 |
| 1.0479 | 0.42 | 5000 | 1.1502 |
| 1.0516 | 0.5 | 6000 | 1.1441 |
| 1.0612 | 0.58 | 7000 | 1.1397 |
| 1.0235 | 0.67 | 8000 | 1.1361 |
| 1.0259 | 0.75 | 9000 | 1.1339 |
| 1.0485 | 0.83 | 10000 | 1.1320 |
| 1.0406 | 0.92 | 11000 | 1.1314 |
| 1.0393 | 1.0 | 12000 | 1.1313 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
