---
library_name: peft
license: apache-2.0
base_model: HuggingFaceTB/SmolVLM-Base
tags:
- generated_from_trainer
model-index:
- name: SmolVLM-Base-vqav2
  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. -->

# SmolVLM-Base-vqav2

This model is a fine-tuned version of [HuggingFaceTB/SmolVLM-Base](https://huggingface.co/HuggingFaceTB/SmolVLM-Base) on an unknown dataset.

## Model description

Here is the sample code for how to use.

```python
from transformers import AutoProcessor, AutoModelForImageTextToText
from peft import PeftModel
import torch
from PIL import Image


DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu" ### DEVICE = "cuda:0" instead of  DEVICE = "cuda"  it fixes flash attention warning!!


model_id = "HuggingFaceTB/SmolVLM-Instruct"  # Base Model

base_model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    _attn_implementation="flash_attention_2" if DEVICE == "cuda" else "eager"
).to(DEVICE)

print(f"Model is on device: {base_model.device}")


# QLoRA adapter
adapter_path = r"C:\Users\.....\SmolVLM-Base-vqav2\checkpoint-670"
model = PeftModel.from_pretrained(base_model, adapter_path)

model = model.to(DEVICE)  # Check the model device #####################################

# Load the processor
processor = AutoProcessor.from_pretrained(model_id)

# Functıon for load images from local
def load_image_from_file(file_path):
    try:
        image = Image.open(file_path)
        return image
    except Exception as e:
        print(f"Error loading image: {e}")
        return None


image1_path = "C:/Users/.../IMG_4.jpg"
image2_path = "C:/Users/.../IMG_35.jpg"

# Load images
image1 = load_image_from_file(image1_path)
image2 = load_image_from_file(image2_path)

# Check the images
if image1 and image2:

    # Create message type
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image"},
                {"type": "image"},
                {"type": "text", "text": "Can you describe and compare the two images?"}
            ]
        },
    ]
    
    # Prepare the Prompt

    prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
    inputs = processor(text=prompt, images=[image1, image2], return_tensors="pt")
    inputs = inputs.to(DEVICE)
    
    # Run the model
    generated_ids = model.generate(**inputs, max_new_tokens=500)
    generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)

    # Print the result
    print(generated_texts[0])  # Çıktı

else:
    print("Images can not be loaded")


```

## 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: Use adamw_hf with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 1

### Training results



### Framework versions

- PEFT 0.14.0
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3