---
base_model: llava-hf/llava-1.5-7b-hf
library_name: peft
license: llama2
metrics:
- bleu
- rouge
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: llava_test
  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. -->

# llava_test

This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0446
- Bleu: 0.6353
- Rouge1: 0.7885
- Rouge2: 0.7889
- Rougel: 0.7893
- Bertscore Precision: 0.6807
- Bertscore Recall: 0.7674
- Bertscore F1: 0.7213

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu   | Rouge1 | Rouge2 | Rougel | Bertscore Precision | Bertscore Recall | Bertscore F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:------:|:-------------------:|:----------------:|:------------:|
| 0.3168        | 10.0  | 10   | 2.2001          | 0.0724 | 0.3123 | 0.1239 | 0.2433 | 0.7068              | 0.7777           | 0.7405       |
| 0.2454        | 20.0  | 20   | 1.6882          | 0.1061 | 0.4044 | 0.1840 | 0.3274 | 0.7241              | 0.7794           | 0.7507       |
| 0.1821        | 30.0  | 30   | 1.1567          | 0.1925 | 0.5281 | 0.2989 | 0.4593 | 0.7054              | 0.7756           | 0.7387       |
| 0.109         | 40.0  | 40   | 0.5242          | 0.3915 | 0.6689 | 0.5316 | 0.6370 | 0.6878              | 0.7709           | 0.7268       |
| 0.0378        | 50.0  | 50   | 0.1193          | 0.5971 | 0.7701 | 0.7585 | 0.7700 | 0.6839              | 0.7688           | 0.7237       |
| 0.0098        | 60.0  | 60   | 0.0554          | 0.6254 | 0.7862 | 0.7867 | 0.7875 | 0.6799              | 0.7694           | 0.7217       |
| 0.0064        | 70.0  | 70   | 0.0482          | 0.6329 | 0.7889 | 0.7890 | 0.7899 | 0.6798              | 0.7690           | 0.7215       |
| 0.0059        | 80.0  | 80   | 0.0459          | 0.6331 | 0.7877 | 0.7877 | 0.7888 | 0.6777              | 0.7670           | 0.7194       |
| 0.0057        | 90.0  | 90   | 0.0451          | 0.6347 | 0.7897 | 0.7895 | 0.7907 | 0.6807              | 0.7675           | 0.7213       |
| 0.0056        | 100.0 | 100  | 0.0446          | 0.6353 | 0.7885 | 0.7889 | 0.7893 | 0.6807              | 0.7674           | 0.7213       |


### Framework versions

- PEFT 0.13.0
- Transformers 4.45.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.20.1