llama-3-86-lora-pretrain_v2
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the sm_artile dataset. It achieves the following results on the evaluation set:
- Loss: 2.2353
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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
2.8142 | 0.1877 | 100 | 2.8566 |
2.7235 | 0.3755 | 200 | 2.6776 |
2.5809 | 0.5632 | 300 | 2.5664 |
2.3971 | 0.7510 | 400 | 2.4458 |
2.4147 | 0.9387 | 500 | 2.3812 |
2.3987 | 1.1265 | 600 | 2.3436 |
2.3 | 1.3142 | 700 | 2.3193 |
2.3219 | 1.5020 | 800 | 2.2951 |
2.377 | 1.6897 | 900 | 2.2763 |
2.2977 | 1.8775 | 1000 | 2.2623 |
2.269 | 2.0652 | 1100 | 2.2525 |
2.2305 | 2.2530 | 1200 | 2.2442 |
2.3866 | 2.4407 | 1300 | 2.2396 |
2.3217 | 2.6285 | 1400 | 2.2369 |
2.2007 | 2.8162 | 1500 | 2.2355 |
Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.19.1
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