| | --- |
| | library_name: peft |
| | license: llama3.1 |
| | base_model: meta-llama/Llama-3.1-8B-Instruct |
| | tags: |
| | - base_model:adapter:meta-llama/Llama-3.1-8B-Instruct |
| | - lora |
| | - transformers |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | - f1 |
| | model-index: |
| | - name: llama3_ft_section_classifier |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # llama3_ft_section_classifier |
| | |
| | This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 3.3342 |
| | - Accuracy: 0.6232 |
| | - Precision: 0.6126 |
| | - Recall: 0.6232 |
| | - F1: 0.6164 |
| | |
| | ## 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: 0.0002 |
| | - train_batch_size: 4 |
| | - eval_batch_size: 4 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 8 |
| | - total_train_batch_size: 32 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: cosine |
| | - lr_scheduler_warmup_ratio: 0.1 |
| | - num_epochs: 20 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | | 16.483 | 1.0 | 275 | 1.3758 | 0.5423 | 0.5769 | 0.5423 | 0.5311 | |
| | | 9.7264 | 2.0 | 550 | 1.1577 | 0.6095 | 0.6215 | 0.6095 | 0.6065 | |
| | | 8.2372 | 3.0 | 825 | 1.1713 | 0.6041 | 0.6264 | 0.6041 | 0.6061 | |
| | | 6.1069 | 4.0 | 1100 | 1.2993 | 0.6123 | 0.6090 | 0.6123 | 0.6025 | |
| | | 3.1467 | 5.0 | 1375 | 1.5804 | 0.6027 | 0.6255 | 0.6027 | 0.6085 | |
| | | 1.3995 | 6.0 | 1650 | 1.9973 | 0.6077 | 0.6005 | 0.6077 | 0.5994 | |
| | | 0.8489 | 7.0 | 1925 | 2.3380 | 0.6082 | 0.6070 | 0.6082 | 0.5990 | |
| | | 0.4705 | 8.0 | 2200 | 2.5919 | 0.6245 | 0.6223 | 0.6245 | 0.6172 | |
| | | 0.186 | 9.0 | 2475 | 2.8240 | 0.6223 | 0.6275 | 0.6223 | 0.6238 | |
| | | 0.0636 | 10.0 | 2750 | 3.0796 | 0.6209 | 0.6273 | 0.6209 | 0.6190 | |
| | | 0.0248 | 11.0 | 3025 | 3.2076 | 0.6259 | 0.6269 | 0.6259 | 0.6231 | |
| | | 0.0009 | 12.0 | 3300 | 3.2148 | 0.6214 | 0.6133 | 0.6214 | 0.6158 | |
| | | 0.0001 | 13.0 | 3575 | 3.2700 | 0.6209 | 0.6132 | 0.6209 | 0.6158 | |
| | | 0.0 | 14.0 | 3850 | 3.2962 | 0.6223 | 0.6124 | 0.6223 | 0.6158 | |
| | | 0.0 | 15.0 | 4125 | 3.3102 | 0.6223 | 0.6118 | 0.6223 | 0.6156 | |
| | | 0.0 | 16.0 | 4400 | 3.3219 | 0.6236 | 0.6138 | 0.6236 | 0.6173 | |
| | | 0.0 | 17.0 | 4675 | 3.3271 | 0.6232 | 0.6125 | 0.6232 | 0.6162 | |
| | | 0.0 | 18.0 | 4950 | 3.3285 | 0.6218 | 0.6108 | 0.6218 | 0.6148 | |
| | | 0.0 | 19.0 | 5225 | 3.3359 | 0.6232 | 0.6126 | 0.6232 | 0.6163 | |
| | | 0.0 | 20.0 | 5500 | 3.3342 | 0.6232 | 0.6126 | 0.6232 | 0.6164 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - PEFT 0.17.1 |
| | - Transformers 4.57.1 |
| | - Pytorch 2.8.0+cu126 |
| | - Datasets 4.0.0 |
| | - Tokenizers 0.22.1 |
