orca-agentinstruct-1M-v1-cleaned
This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B on the mlabonne/orca-agentinstruct-1M-v1-cleaned dataset. It achieves the following results on the evaluation set:
- Loss: 0.4721
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: 32
- gradient_accumulation_steps: 2
- total_train_batch_size: 512
- total_eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 1738
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4816 | 0.9994 | 908 | 0.4857 |
| 0.4296 | 2.0 | 1817 | 0.4724 |
| 0.3803 | 2.9983 | 2724 | 0.4721 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.3.0
- Datasets 2.21.0
- Tokenizers 0.20.3
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