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README.md
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---
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library_name: peft
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license: llama3.1
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base_model: meta-llama/Llama-3.1-8B-Instruct
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tags:
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- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
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- lora
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- transformers
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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model-index:
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- name: llama3-section-classifier
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# llama3-section-classifier
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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.
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It achieves the following results on the evaluation set:
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- Loss: 1.1236
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- Accuracy: 0.6536
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- Precision: 0.6613
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- Recall: 0.6536
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- F1: 0.6553
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0002
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 32
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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| 9.6616 | 1.0 | 310 | 1.1046 | 0.6464 | 0.6476 | 0.6464 | 0.6244 |
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| 7.0864 | 2.0 | 620 | 1.0653 | 0.64 | 0.6520 | 0.64 | 0.6414 |
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| 4.771 | 3.0 | 930 | 1.1236 | 0.6536 | 0.6613 | 0.6536 | 0.6553 |
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### Framework versions
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- PEFT 0.17.1
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- Transformers 4.57.1
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- Pytorch 2.8.0+cu126
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- Datasets 4.0.0
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- Tokenizers 0.22.1
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