metadata
library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: LLama3-finetuning
results: []
LLama3-finetuning
This model is a fine-tuned version of meta-llama/Llama-3.2-1B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3923
- Accuracy: 0.8414
- F1 Macro: 0.8365
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-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|---|---|---|---|---|---|
| 1.8719 | 1.0 | 454 | 0.8635 | 0.6562 | 0.6261 |
| 0.9455 | 2.0 | 908 | 0.4734 | 0.8168 | 0.8068 |
| 0.7437 | 3.0 | 1362 | 0.4071 | 0.8366 | 0.8305 |
| 0.7825 | 4.0 | 1816 | 0.3959 | 0.8433 | 0.8391 |
| 0.6047 | 5.0 | 2270 | 0.3910 | 0.8400 | 0.8341 |
Framework versions
- PEFT 0.14.0
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0