README.md

---
base_model: NousResearch/Llama-2-7b-hf
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: legal-document-summarization
  results: []
pipeline_tag: summarization
language:
- en
---

<!-- 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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/sindhujagovindaraj2003-sri-eshwar-college-of-engineering/huggingface/runs/4g40njrw)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/sindhujagovindaraj2003-sri-eshwar-college-of-engineering/huggingface/runs/4g40njrw)
# legal-document-summarization

This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2231

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2845        | 0.4   | 8    | 1.2406          |
| 1.2368        | 0.8   | 16   | 1.2264          |
| 1.0085        | 1.2   | 24   | 1.2255          |
| 0.9203        | 1.6   | 32   | 1.2244          |
| 0.9852        | 2.0   | 40   | 1.2231          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1