|
--- |
|
license: apache-2.0 |
|
datasets: |
|
- databricks/databricks-dolly-15k |
|
language: |
|
- en |
|
pipeline_tag: text-generation |
|
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T |
|
--- |
|
|
|
TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T finetuned using dolly dataset. |
|
|
|
Training took 1 hour on an 'ml.g5.xlarge' instance. |
|
|
|
|
|
```python |
|
hyperparameters ={ |
|
'num_train_epochs': 3, # number of training epochs |
|
'per_device_train_batch_size': 6, # batch size for training |
|
'gradient_accumulation_steps': 2, # Number of updates steps to accumulate |
|
'gradient_checkpointing': True, # save memory but slower backward pass |
|
'bf16': True, # use bfloat16 precision |
|
'tf32': True, # use tf32 precision |
|
'learning_rate': 2e-4, # learning rate |
|
'max_grad_norm': 0.3, # Maximum norm (for gradient clipping) |
|
'warmup_ratio': 0.03, # warmup ratio |
|
"lr_scheduler_type":"constant", # learning rate scheduler |
|
'save_strategy': "epoch", # save strategy for checkpoints |
|
"logging_steps": 10, # log every x steps |
|
'merge_adapters': True, # wether to merge LoRA into the model (needs more memory) |
|
'use_flash_attn': True, # Whether to use Flash Attention |
|
} |
|
|
|
``` |
