---
license: llama2
datasets:
- REILX/text-description-of-the-meme
language:
- en
- zh
tags:
- llava
- lora
---
<style>
.img-responsive {
  width: 50%;
  height: auto;
}
</style>

### Conclusion
While significantly better at understanding and describing emotions and details in images compared to LLaVA-1.5-7b-hf, the fine-tuned model struggles with recognizing text.

### Train Loss
![](./adapter-module/training_loss.png)

### Test
A comparative analysis of emoji in prompts, differents between the original model and its fine-tuned counterpart. </br>
Original Model:https://huggingface.co/llava-hf/llava-1.5-7b-hf/</br>
<img src="./images/original-01.JPG" alt="meme01" class="img-responsive">
<img src="./images/original-02.JPG" alt="meme02" class="img-responsive">
<img src="./images/original-03.JPG" alt="meme03" class="img-responsive">


Fine-tuned Lora Model:https://huggingface.co/REILX/llava-1.5-7b-hf-meme-lora</br>
<img src="./images/lora-01.JPG" alt="meme01" class="img-responsive">
<img src="./images/lora-02.JPG" alt="meme02" class="img-responsive">
<img src="./images/lora-03.JPG" alt="meme03" class="img-responsive">

### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- cutoff_len: 2048
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 5.0