---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
license: llama3.1
tags:
- generated_from_trainer
model-index:
- name: Llama-3.1-8B-Instruct-JudicialSummarization-sci-textrank-FinetuneLlama3.18b
  results: []
---

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# Llama-3.1-8B-Instruct-JudicialSummarization-sci-textrank-FinetuneLlama3.18b

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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.2441

## 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: 2.5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 6.312         | 0.7179 | 500  | 6.3024          |
| 6.2191        | 1.4358 | 1000 | 6.2649          |
| 6.2087        | 2.1536 | 1500 | 6.2514          |
| 6.2002        | 2.8715 | 2000 | 6.2441          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.4.0+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1