oh_scale_x4_compute_equal
This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B on the mlfoundations-dev/oh-dcft-v1.3_no-curation_gpt-4o-mini_scale_4x dataset. It achieves the following results on the evaluation set:
- Loss: 0.7223
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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- total_eval_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- num_epochs: 4.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7336 | 0.9996 | 996 | 0.7307 |
| 0.6923 | 1.9992 | 1992 | 0.7167 |
| 0.6521 | 2.9999 | 2989 | 0.7147 |
| 0.6204 | 3.9985 | 3984 | 0.7223 |
Framework versions
- Transformers 4.46.1
- Pytorch 2.3.0
- Datasets 3.1.0
- Tokenizers 0.20.3
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Base model
meta-llama/Llama-3.1-8B