rinko_300_labeling
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.0068
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: 2e-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.3912 | 0.9897 | 48 | 2.2464 |
| 2.2442 | 2.0 | 97 | 2.1167 |
| 2.1047 | 2.9897 | 145 | 2.0317 |
| 2.05 | 4.0 | 194 | 2.0067 |
| 2.0626 | 4.9485 | 240 | 2.0068 |
Framework versions
- PEFT 0.7.1
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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Base model
meta-llama/Meta-Llama-3-8B-Instruct