---
base_model: google/paligemma-3b-pt-224
library_name: peft
license: gemma
tags:
- generated_from_trainer
model-index:
- name: paligemma_vqav3
  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. -->
# Inference
``` py
model_id = "Ohmmy3847/paligemma_vqav3"
model = PaliGemmaForConditionalGeneration.from_pretrained("Ohmmy3847/paligemma_vqav3")
processor = AutoProcessor.from_pretrained("google/paligemma-3b-pt-224")
prompt = "อธิบายภาพนี้เป็นภาษาไทย"

raw_image = Image.open("<Your Image>.jpg")

inputs = processor(prompt, raw_image.convert("RGB"), return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=20)

print(processor.decode(output[0], skip_special_tokens=True)[len(prompt):])
raw_image


```

# paligemma_vqav3

This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on an unknown dataset.

## 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: 2e-05
- 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: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 2

### Training results



### Framework versions

- PEFT 0.11.2.dev0
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1