---
license: mit
base_model: microsoft/git-base
tags:
- generated_from_trainer
model-index:
- name: git-base-pokemon
  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. -->

#dataset used: polinaeterna/pokemon-blip-captions

#code
```python

from transformers import AutoProcessor, AutoModelForCausalLM
import torch
from PIL import Image
import requests

#Preprocess the dataset
#Since the dataset has two modalities (image and text), the pre-processing pipeline will preprocess images and the captions.
#To do so, load the processor class associated with the model you are about to fine-tune.

from transformers import AutoProcessor

checkpoint = "microsoft/git-base"
processor = AutoProcessor.from_pretrained(checkpoint)

device = "cuda" if torch.cuda.is_available() else "cpu"

model_name = "kr-manish/git-base-pokemon"  # Replace with your actual username and model name
model = AutoModelForCausalLM.from_pretrained(model_name).to(device)

url =  "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"  # Replace with the URL of your image
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(images=image, return_tensors="pt").to(device)

generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50)
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_caption)

#a pink and purple pokemon character with big eyes

```

# git-base-pokemon

This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5797
- Wer Score: 8.9592

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Score |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 8.155         | 4.17  | 50   | 6.4318          | 25.1325   |
| 5.3386        | 8.33  | 100  | 4.0782          | 18.6484   |
| 3.3109        | 12.5  | 150  | 2.4303          | 9.4306    |
| 2.0471        | 16.67 | 200  | 1.5797          | 8.9592    |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2