---
base_model: unsloth/llama-3-8b-bnb-4bit
library_name: peft
license: llama3
tags:
- trl
- sft
- unsloth
- generated_from_trainer
model-index:
- name: judicial-summarization-llama-3-finetuned_mildsum_TR
  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. -->

# judicial-summarization-llama-3-finetuned_mildsum_TR

This model is a fine-tuned version of [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0157

## 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: 2
- eval_batch_size: 8
- seed: 3407
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5351        | 1.0   | 273  | 1.6661          |
| 1.3986        | 2.0   | 546  | 1.6676          |
| 1.3575        | 3.0   | 819  | 1.7026          |
| 1.2119        | 4.0   | 1092 | 1.7920          |
| 1.0078        | 5.0   | 1365 | 1.8972          |
| 0.7387        | 6.0   | 1638 | 2.0157          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1